Recent Research Articles in Security

International Journal of Computer Networks & Communications (IJCNC)

(Scopus, ERA Listed)

ISSN 0974 – 9322 (Online); 0975 – 2293 (Print)

http://airccse.org/journal/ijc2018.html

Improvement of False Report Detection Performance Based on Invalid Data Detection Using Neural Network in WSN

Sanghyeok Lim and Taeho Cho

Department of Electrical and Computer Engineering, Sungkyunkwan University, Republic of Korea

ABSTRACT

WSN consists of a number of nodes and base stations and is used for event monitoring in various fields such as war situations, forest fires, and home networks. WSN sensor nodes are placed in fields that are difficult for users to manage. It is therefore vulnerable to attackers, and attackers can use false nodes or MAC injection attacks through the hijacked nodes to reduce the lifetime of the network or trigger false alarms. In order to prevent such attacks, several security protocols have been proposed, and all of them have been subjected to MAC-dependent validation, making it impossible to defend against false report attacks in extreme attack circumstances. As attacks have recently become more diverse and more intelligent, WSNs require intelligent methods of security. Based on the report information gathered from the base station, the proposed method provides a technique to prevent attacks that may occur where all MAC information is damaged by carrying out verification of a false report attack through the machine learning based prediction model and the evaluation function.

KEYWORDS

Network Protocols, Wireless Sensor Network, simulation, machine learning, neural network

For More Details: http://aircconline.com/ijcnc/V10N6/10618cnc02.pdf

Volume Link: http://airccse.org/journal/ijc2018.html

REFERENCES

[1]  Al-Karaki, Jamal N., and Ahmed E.Kamal. “Routing techniques in wireless sensor networks: a survey.” IEEE wireless communications 11.6 (2004): 6-28.

[2]   Perrig, Adrian, John Stankovic, and David Wagner. “Security in wireless sensor networks.” Communications of the ACM 47.6.

[3]  Karlof, Chris, and David Wagner. “Secure routing in wireless sensor networks: Attacks and countermeasures.” Sensor Network Protocols and Applications, 2003. Proceedings of the First IEEE. 2003 IEEE International Workshop on. IEEE, 2003.

[4]  Padmavathi, Dr G., and Mrs Shanmugapriya. “A survey of attacks, security mechanisms and challenges in wireless sensor networks.” arXiv preprint arXiv:0909.0576 (2009).

[5]  Pathan, Al-Sakib Khan, Hyung-Woo Lee, and Choong Seon Hong. “Security in wireless sensor networks: issues and challenges.” Advanced Communication Technology, 2006. ICACT 2006. The 8th International Conference. Vol. 2. IEEE, 2006.

[6]  Li, Feng, and Jie Wu. “A probabilistic voting-based filtering scheme in wireless sensor networks.“Proceedings of the 2006 international conference on Wireless communications and mobile computing. ACM, 2006

[7]    Zhu, Sencun, et al. “An interleaved hop-by-hop authentication scheme for filtering of injected false data in sensor networks.” Security and privacy, 2004. Proceedings. 2004 IEEE symposium on. IEEE,2004

[8]    Yang, Hao, and Songwu Lu. “Commutative cipher based en-route filtering in wireless sensor networks.” Vehicular Technology Conference, 2004. VTC2004-Fall. 2004 IEEE 60th. Vol. 2. IEEE, 2004.

[9]   Yu, Zhen, and Yong Guan. “A dynamic en-route scheme for filtering false data injection in wireless sensor networks.” Proceedings of the 3rd international conference on Embedded networked sensor systems. ACM, 2005

[10]  Hagan, Martin T., et al. Neural network design. Vol. 20. Boston: Pws Pub., 1996.

[11]   Adeli, Hojjat, and Shih-Lin Hung. Machine learning: neural networks, genetic algorithms, and fuzzy systems. John Wiley & Sons, Inc., 1994.

[12]  Haykin, Simon S., et al. Neural networks and learning machines. Vol. 3. Upper Saddle River, NJ, USA:: Pearson, 2009.

[13] Weiss, Sholom M., and Casimir A. Kulikowski. Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems. Morgan Kaufmann Publishers Inc., 1991.

[14] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. “Imagenet classification with deep convolutional neural networks.” Advances in neural information processing systems. 2012.

[15]  Kubat, Miroslav, Robert C. Holte, and Stan Matwin. “Machine learning for the detection of oil spills in satellite radar images.” Machine learning 30.2-3 (1998): 195-215.

[16]  Sebastiani, Fabrizio. “Machine learning in automated text categorization.” ACM computing surveys (CSUR) 34.1 (2002): 1-47.

[17] Bradley, Andrew P. “The use of the area under the ROC curve in the evaluation of machine learning algorithms.” Pattern recognition 30.7 (1997): 1145-1159.

[18]  Nasrabadi, Nasser M. “Pattern recognition and machine learning.” Journal of electronic

[19]  Bak, Per, Kan Chen, and Chao Tang. “A forest-fire model and some thoughts on turbulence.” Physics letters A 147.5-6 (1990): 297-300.

[20]  Preisler, Haiganoush K., and Alan A. Ager. “ForestFire Models.” Encyclopedia of environmetrics 3 (2006).

[21]  Anderson, D. H., et al. “Modelling the spread of grass fires.” The ANZIAM Journal 23.4 (1982): 451- 466.

[22]  Weber, R. O. “Modelling fire spread through fuel beds.” Progress in Energy and Combustion Science 17.1 (1991): 67-82.

[23]   Soares-Filho, Britaldo Silveira, Gustavo Coutinho Cerqueira, and Cássio Lopes Pennachin. “DINAMICA—a stochastic cellular automata model designed to simulate the landscape dynamics in an Amazonian colonization frontier.” Ecological modelling 154.3 (2002): 217-235.

[24]  Mallet, Daniel G., and Lisette G. De Pillis. “A cellular automata model of tumor–immune system interactions.” Journal of theoretical biology 239.3 (2006): 334-350.

[25] Dijkstra, Jan, Joran Jessurun, and Harry JP Timmermans. “A multi-agent cellular automata model of pedestrian movement.” Pedestrian and evacuation dynamics (2001): 173-181.

[26] Karafyllidis, Ioannis, and Adonios Thanailakis. “A model for predicting forest fire spreading using cellular automata.” Ecological Modelling 99 (1997): 87-97.

[27] Encinas, A. Hernández, et al. “Simulation of forest fire fronts using cellular automata.” Advances in Engineering Software38.6 (2007): 372-378.

[28] Karafyllidis, Ioannis. “Design of a dedicated parallel processor for the prediction of forest fire spreading using cellular automata and genetic algorithms.” Engineering Applications of Artificial Intelligence 17.1 (2004): 19-36.

[29] Wolfram, Stephen. “Universality and complexity in cellular automata.” Physica D: Nonlinear Phenomena 10.1-2 (1984): 1-35.

[30] Nam, Su Man, and Tae Ho Cho. “Context-aware architecture for probabilistic votingbased filtering scheme in sensor networks.” IEEE Transactions on Mobile Computing 16.10 (2017): 2751-2763.

[31] Song and Lee, 2013: Sensitivity Analysis on Ecological Factors Affecting Forest Fire Spreading: Simulation Study Korean Journal of Agricultural and Forest Meteorology, Vol. 15, No. 3 pp. 178~185

[32] Agostinelli, Forest, et al. “Learning activation functions to improve deep neural networks.” arXiv preprint arXiv:1412.6830 (2014).

[33]  Le, Quoc V., et al. “On optimization methods for deep learning. Proceedings of the 28th International Conference on International Conference on Machine Learning. Omnipress, 2011.

[34]  Srivastava, Nitish, et al. “Dropout: a simple way to prevent neural networks from over fitting.” The Journal of Machine Learning Research 15.1 (2014): 1929-1958.

[35]  Munir, Saad Ahmed, et al. “Mobile wireless sensor network: Architecture and enabling technologies for ubiquitous computing.” Advanced Information Networking and Applications Workshops, 2007, AINAW’07. 21st International Conference on. Vol. 2. IEEE, 2007

AUTHOR

author-1

Sanghyeok Lim Received a B.S. degree in Digital Information Engineering from Hanguk University of Foreign Studies in 2017, and is now working toward an M.S. degree in the Department of Electrical and Computer Engineering at Sungkyunkwan University.

author-2

Taeho Cho Received a Ph.D. degree in Electrical and Computer Engineering from the University of Arizona, USA, in 1993, and B.S. and M.S. degrees in Electrical and Computer Engineering from Sungkyunkwan University, Republic of Korea, and the University of Alabama, USA, respectively. He is currently a Professor in the College of Information and Communication Engineering, Sungkyunkwan University, Korea.

Availability Aspects through Optimization Techniques Based Outlier Detection Mechanism in Wireless and Mobile Networks

Neeraj Chugh, Adarsh Kumar and Alok Aggarwal

School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India

ABSTRACT

Radio Frequency Identification (RFID) and Wireless Sensor Networks (WSN) are the two most prominent wireless technologies for implementing a complete smart environment for the Internet of Things (IoT). Both RFID and WSN are resource constraint devices, which forces us to go for lightweight cryptography for security purposes. Security in terms of confidentiality, integrity, authentication, authorization, and availability. Key management is one of the major constraints for resource constraint mobile sensor devices. This work is an extension of the work done by Kumar et al. using efficient error prediction and limit of agreement for anomaly score. This work ensures cryptographic property, availability, in RFID-WSN integrated network through outlier detection mechanism for 50 to 5000 nodes network. Through detection ratios and anomaly scores system is tested against outliers. The proposed outlier detection mechanism identifies the inliers and outliers through anomaly score for protection against Denial-of-Service (DoS) attack. Intruders can be detected in few milliseconds without giving any conflict to the access rights. In terms of throughput, a minimum improvement of 6.2% and a maximum of 219.9% is observed for the proposed protocol as compared to Kumar et al. Protocol and in terms of percentage of Packet Delivery Ratio (PDR), a minimum improvement of 8.9% and a maximum of 19.5% is observed for the proposed protocol as compared to Kumar et al. protocol.

KEYWORDS

Wsn, Manet, Rfid, Anomaly, Security

For More Details:  http://aircconline.com/ijcnc/V10N6/10618cnc05.pdf

 Volume Link: http://airccse.org/journal/ijc2018.html

REFERENCES

[1]  A. Kumar, K. Gopal and A. Aggarwal, “Outlier Detection and Treatment for Lightweight Mobile Ad Hoc Networks,” in In International Conference on Heterogeneous Networking for Quality,Reliability, Security and Robustness, Greater Noida,India,11-12 January 2013, pp.750-763.

[2]  A. Kumar, A. Agarwal and Charu, “Efficient Hierarchical Threshold Symmetric Group Key Management Protocol for Mobile Ad Hoc Networks,”Inter. Conf. on Contemporary Computing – IC3 2012, Noida,India,6-8 August 2012, pp. 335-346.

[3]  D. Bonch and M. Franklin, “Identity-based encryption from weil pairing,” Advances in CryptologyCrypto 2001,Santa Barbara, California, USA,August 2001, pp. 213-229.

[4]   Merwe, J. V. D., D. D. and M. S, ” A survey on peer-to-peer key management for mobile ad hoc networks,” ACM computing surveys (CSUR), Vol. 39, No. 1, 1, April 2007, pp. 1-45.

[5]   H. Deng, A. Mukherjee and D. Aggarwal, “Threshold and identity based key management and authentication for wireless ad hoc networks,” in International conference on information technology: Coding and Computing(ITCC’s 04), Las Vegas, Nevada, April 2004, pp. 1-5.

[6]  Y. Zhang, W. Liu, W. Lou and Y. Fang, “Securing mobile ad hoc networks with certificateless public keys,” IEEE Transaction on Dependable and Secure Computing, Vol. 3, No. 4,Dec. 2006, pp. 386-399.

[7] H. Harney, C. Muckenhirn, “Group key management protocol (GKMP) architecture“, Network Working Group, July 1997.[Online].Available:https://www.rfc-editor.org/info/rfc2094. [Accessed: Jan. 1, 2018].

[8] H. Harney, C. Muckenhirn, “Group Key Management Protocol(GKMP) Specification” ,Internet Request for Comments 2093,” July 1997.[Online]. Available:https://www.rfc-editor.org/info/rfc2093. [Accessed: Jan. 1, 2018].

[9]   H. Harney ,U. Meth, A. Colegrove and G. Gross, “Group Secure Association Key Management Protocol(GKMP)“, Internet Request for Comments 4535,” June 2006. [Online]. Available:https://www.rfc editor.org/info/rfc4535. [Accessed: Jan. 1, 2018].

[10]  B. Weis, S. Rowles and T. Hardjono, “The Group Domain of Interpretation(GDOI)”,Internet Request for Comments 6407, Oct.2011.[Online]. Available:https://www.rfc-editor.org/info/rfc6407. [Accessed: Jan. 1, 2018].

[11]   Bryans, J. W., Fitzgerald and J. S., “Formal engineering of XACML access control policies in VDM++,” in International Conference on Formal Engineering Methods, Florida, USA, Berlin, Heidelberg, Nov. 2007, pp. 37–56.

[12]   K. Fisler, S. Krishnamurthi, L. A. Meyerovich and M. C. Tschantz, “Verification and change-impact analysis of access control policies,” in Proc. of 27th International Conference on Software Engineering, MO, USA,May 2005, pp. 196-205.

[13]  D. Jackson, , Software Abstractions: Logic, Languages, and Analysis, MIT Press, ISBN: 978-0-262- 10114-1, 2006. .

[14]  D. Jackson, “Micromodels of Software: Lightweight Modelling and Analysis with Alloy,” MIT Lab, Jan. 2002. [Online]. Available:https://courses.cs.washington.edu/courses/cse503/04sp/readings/alloyref.pdf. [Accessed: Jan. 1, 2018].

[15]  D. Jackson, “Alloy: a lightweight object modelling notation,” ACM Trans. Soft. Eng. Methodol., Vol. 11, No. 2, April 2002, pp. 256-290.

[16]  V. Chandola, A. Banerjee and V. Kumar, “Anomaly Detection: A Survey,” ACM computing surveys, Vol. 41, No. 3, 2009, pp. 1-72.

[17]  Y. Zhang, N. Meratnia and P. Havinga, “Outlier Detection Techniques for Wireless Sensor Networks: A Survey,” IEEE Communication Surveys & Tutorials, Vol. 12, No. 2, 2010, pp. 159-170.

[18]  P. Gogoi, B. Borah and D. K. Bhattacharyya, “Anomaly Detection Analysis of Intrusion Data using Supervised and Unsupervised Approach,” Journal of Convergence Information Technology, Vol. 5, No. 1, Feb.2010, pp. 95-110.

[19]  P. Gogoi, D. K. Bhattacharyya, B. Borah and J. K. Kalita, A Survey of Outlier Detection Methods in Network Anomaly Identification,” The Computer Journal, Vol. 54, No. 4, April 2011, pp. 570-588.

[20]  D. M. Hawkin, Identification of Outliers, London: Chapman and Hall, 1980.

[21]  V. A. Traag, A. Browet, F. Calabrese and F. Morlot, “Social Event Detection in Massive Mobile Phone Data Using Probabilistic Location Interference,” in SocialCom/PASSAT, 9-11 October 2011.

[22]  B. Krishnamachari and S. Iyengar, “Distributed Bayesian algorithms for fault tolerant event region detection in wireless sensor networks,” IEEE Transactions on Computers, Vol. 53, No. 3, March 2004, pp. 241-250.

[23]  F. Martincic and L. Schwiebert, “Distributed event detection in sensor networks,” in Proceedings of Systems and Network Communication, French, Polynesia, Nov. 2006, pp. 1-6.

[24] M. Ding, D. Chen, K. Xing and X. Cheng, Localized fault tolerant event boundary detection in sensor networks,” in IEEE conference of computer and communications socities, Florida, USA, March 2005, pp. 902-913.

[25] A. P. R. Silva, M. H. T. Martins, B. P. S. Rocha, A. A. F. Loureiro, L. B. Ruiz and H. C. Wong, “Decentralized intrusion detection in wireless sensor networks,” 1st ACM international workshop on Quality of Service and Security in Wireles., Quebec, Canada Oct. 2005, pp. 16-23.

[26] J. Chen, S. Kher and A. Somani, “Distributed fault detection of wireless sensor networks,“Proceedings of the 2006 workshop on dependability issues in wireless ad hoc networks and sensor networks, CA, USA, Sep. 2006, pp. 65-72.

[27] X. Luo, M. Dong and Y. Huang, “On distributed fault tolerant detection in wireless sensor networks,” IEEE Transactions on computers, Vol. 55, No.1, Jan. 2006 pp. 58-70.

[28] J. Raja, X. R. Wang, O. Obst and P. Valencia, “Wireless sensor network anomalies: Diagnosis and detection strategies,“Intelligence-Based Systems Engineering, Berlin, Heidelberg, 2011, pp. 309-325.

[29] W. Hu, T. Tan, L.Wang and S. Maybank, “A survey on visual surveillance of object motion and behaviors,” IEEE transavtion, Vol. 34, No. 3,July 2004, pp. 334-352.

[30] D. M. Hawkins, Ident fication of outliers, London: Chapman and Hall, 1980.

[31] E. M. Knorr and R. T. Ng, “Algorithm for mining distance based outliers in large datasets,” 24th international conference on very large databases, New York, USA, 1998, pp. 392-403.

[32] M. M. Breunig, H. P. Kriegel, R. T. Ng and J. Sander, “LOF: Identifying Density Based Local Outliers,” ACM SIGMOD, Dallas, TX, USA, May 2000, pp. 93-104.

[33] B. Wang and W. Perrizo, “RDF: a density-based outlier detection method using vertical data representation,” in Fourrth IEEE InternationalConference on Data Mining, Nov. 2004, pp. 1-4.

[34] S. Rajagopalan, R. Karwoski, B. Bartholmai and R. Robb, “Quantitative image analytics for strtified pulmonary medicine,” in IEEE Int. Symposium on Biomedical Imaging (ISBI), Barcelona, Spain,May 2012, pp. 1779-1782.

[35] J. W. Branch, C. Giannelia, B. Szymanski, R. Wolff and H. Kargupta, “In-network outlier detection in wireless sensor networks,” Knowledge and information systems, Vol. 34, No. 1, Jan. 2013, pp. 23-54.

[36] H. Ayadi, A. Zouinkhi and B. Boussaid, “A Machine Learning Methods: Outlier detection in WSN,” in 16th international conference on Sciences and Techniques of Automatic control, Monastir, Tunisia, December 2015, pp. 722-727.

[37] C. Titouna, M. Aliouat and M. Gueroui, “Outlier Detection Approach Using Bayes Classifiers,” Wireless Pers. Communications, Vol. 85, No. 3, June 2015, pp. 1009-1023.

[38] J. C. M. Teo and C. H. Tan, “Energy-efficient and scalable group key agreement for large ad hoc networks,” in Proceedings of the 2nd ACM international workshop on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks, Quebec, Canada, October 2005, pp. 114-121.

[39] E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numerische Mathematik, Vol. 1, No. 1, December 1959,pp. 269-271.

[40] J. Spinrad, “Recognition of circle graphs,” Journal of Algorithms, Vol. 16, No. 2, March 1994, pp. 264-282.

[41] W. J. Gutjahr, “A graph-based Ant System and its convergence,” Future Generation Computer Systems, Vol. 16, No. 9, June 2000, pp. 873-888.

[42] A. Shamir, “How to share a secret,” Communications of the ACM, Vol. 22, No. 11, November 1979, pp. 612- 613.

[43] J. V. D. Merwe, D. Dowoud and S. McDonald, “A Survey on Peer to Peer key management for Mobile Ad Hoc Networks,” ACM Computing Surveys, Vol. 39, No. 1, Article 1, April 2007, pp. 1-45.

[44]”The Network Simulator – ns-2,” [Online]. Available: https://www.isi.edu/nsnam/ns/. [Accessed 18 7 2018].

[45]  “scipy.cluster.hierarchy.dendrogram.html,”[Online].Available: https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.dendrogram.html. [Accessed 18 7 2018].

[46] A. Kumar, K. Gopal and A. Aggarwal,” Novel Trusted Hierarchy Construction for RFID Sensor– Based MANETs Using ECCs,” ETRI Journal, Vol. 37, No. 1, July 2015, pp. 186-196.

[47] A. Kumar, K. Gopal and A. Aggarwal, ” Simulation and analysis of authentication protocols for mobile Internet of Things (MIoT),” 2014 IEEE International Conference on Parallel, Distributed and Grid Computing (PDGC), JUIT, Waknaghat, India, 2014, pp.423-428.

[48] A. Kumar, K. Gopal and A. Aggarwal, “Design and Analysis of Lightweight Trust Mechanism for Accessing Data in MANETs,” KSII Transactions on Internet & Information Systems, Vol. 8, No. 3, March 2014, pp. 1119-1143.

[49] A. Kumar, K. Gopal and A. Aggarwal, “Cost and Lightweight Modeling Analysis of RFID Authentication Protocols in Resource Constraint Internet of Things,” Journal of Communications Software and Systems, Vol. 10, No. 3, September 2014, pp. 179-143.

[50] A. Kumar, K. Gopal and A. Aggarwal, ” A complete, efficient and lightweight cryptography solution for resource contrainsts Mobile Ad-Hoc Networks”, 2nd IEEE International Conference on Parallel, Distributed and Grid Computing (PDGC), JUIT, Waknaghat, India, Feb. 2013, pp. 854-860.

[51] A. Kumar, K. Gopal and A. Aggarwal,” Design and Analysis of Lightweight Trust Mechanism for Secret Data using Lightweight Cryptographic Primitives in MANETs”, IJ Network Security, Vol. 18, No. 1, Jan. 2016, pp.1-18.

[52] A. Kumar, K. Gopal and A. Aggarwal,”A novel lightweight key management scheme for RFIDsensor integrated hierarchical MANET based on internet of things”, International Journal of Advanced Intelligence Paradigms, Vol. 9, No. 2-3, 2017, pp. 220-245.

[53] N. Chugh, A. Kumar and A. Aggarwal, “Security aspects of a RFID-sensor integrated low-powered devices for internet-of-things”, 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), JUIT, Waknaghat, India, Dec. 2016, pp. 759-763.

AUTHORS

author-1

Neeraj Chugh is an Assistant Professor in University of Petroleum & Energy Studies, Dehradun, India and enrolled in PhD (CSE) from Uttrakhand Technical University (UTU), Uttrakhand, India. He received his M. Tech. (CSE) from Kurukshetra University Kurukshetra, India in 2001. His research interests includes Database   Management system, Data Mining, and Outlier/Anomaly detection and event detection in sensor networks.

author-2

Adarsh Kumar received his ME degree in Software Engineering from Thapar University, Patiala, Punjab, India, in 2005 and earned his PhD degree from JIIT university, Noida, India in 2016 followed by Post Doctoral from AIT, Ireleand during 2016-2018. From 2005 to 2016, he has been associated with the Department of Computer Science Engineering & Information Technology, Jaypee Institute of  Information Technology, Noida, UttarPardesh, India, where he worked as Assistant Professor. Currently he is working with University of Petroleum & Energy Studies, Dehradun, India as Associate Professor in CSE department. His main research interests are cryptography, network security, and adhoc networks.

author-3

Alok Aggarwal received his bachelors’ and masters’ degrees in Computer Science& Engineering in 1995 and 2001 respectively and his PhD degree in Engineering from IITRoorkee, Roorkee, India in 2010. He has academic experience of 18 years,  industry experience of 4 years and research experience of 5 years. He has contributed more than 150 research contributions in different journals and conference proceedings. Currently he is working with University of Petroleum & Energy Studies, Dehradun, India as Professor in CSE department. His main research interests are wired/wireless networks, security, and coding theory.

Improvement of Multiple Routing Based on Fuzzy Clustering and pso Algorithm in Wsns to Reduce Energy Consumption

Gholamreza Farahani

Department of Electrical Engineering and Information Technology, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran

 ABSTRACT

One of the most important issues discussed in Wireless Sensor Networks (WSNs) is how to transfer information from nodes within the network to the base station and select the best possible route for transmission of this information, taking into account energy consumption for the network lifetime with maximum reliability and security. Hence, it would be useful to provide a suitable method that would have the features mentioned. This paper uses an Ad-hoc On-demand Multipath Distance Vector (AOMDV) as a routing protocol. This protocol has high energy consumption due to its multipath. However, it is a big challenge if it can reduce AOMDV energy consumption. Therefore, clustering operations for nodes are of high priority to determine the head of clusters which LEACH protocol and fuzzy logic and Particle Swarm Optimization (PSO) algorithm are used for this purpose. Simulation results represent 5% improvement in energy consumption in a WSN compared to AOMDV method.

KEYWORDS

Energy Aware Routing Protocol, Fuzzy Logic, Ad-hoc Multipath, LEACH, Particle Swarm Optimization Algorithm

For More Details : http://airccse.org/journal/cnc/0910ijcnc06.pdf

Volume Link : http://airccse.org/journal/ijc2018.html

 REFERENCES

[1] Heinzelman, W. R., Chandrakasan, A. & Balakrishnan, H., (2000) “Energy-Efficient Communication Protocol for Wireless Microsensor Networks”, Proceedings of the Hawaii International Conference on System Sciences, 4-7 January, Maui, Hawaii.

[2]  Houda, L. (2010) Wireless ad hoc and Sensor Networks, Vol. 6, John Wiley & Sons.

[3]  Marina, M. K. & Das, S. R., (2001) “On-demand Multipath Distance Vector Routing for Ad Hoc Networks”, Proceeding of 9th IEEE International Conference on Network Protocols, pp14-23, Riverside, 11-14 November, California, USA.

[4]  Amine, D. A., Kamel, A. M. & Bouabdellah, K., (2014) “Formal Verification of a New Version of AOMDV in ad hoc Network”, Procedia Computer Science, Vol. 37, pp160-167.

[5]  Dam, M. T., Nguyen, V. C., Nguyen, T. T. & Tran Le, T. D., (2014) “Low-Power and HighPerformance Design for Cryptosystem Using Power Aware and Pipeline Techniques”, International Conference on Advanced Technologies for Communications (ATC), 15-17 October, Hanoi, Vietnam.

[6] Kevin, J. (2009) Security and Privacy Controls for Federal Information Systems and Organizations, Revision 3, NIST SP. 800–53.

[7]  Nguyen, T. T., Nguyen, V. C. & Pham, H. M., (2012) “Enhance the performance and security of SOC using pipeline and dynamic partial reconfiguration”, International Conference on Integrated Circuits and Devices in Vietnam (ICDV), 13-15 August, Danang, Vietnam.

[8]  Rages, G. K. & Baskaran, K., (2012) “A Survey on Futuristic Health Care System: WBANs”, Procedia Engineering, Vol. 30, pp889–896.

[9] Singh, R. & Verma, A. K., (2017) “Energy efficient cross layer based adaptive threshold routing protocol for WSN”, AEU – International Journal of Electronics and Communications, Vol. 72, pp166-173.

[10]  Ke, W., Yangrui, O., Hong, J., Heli, Z. & Xi, L., (2016) “Energy aware hierarchical cluster-based routing protocol for WSNs”, The Journal of China Universities of Posts and Telecommunications, Vol. 23, Issue 4, pp46-52.

[11] Yigit, M., Gungor, V. C., Fadel, E., Nassef, L., Akkari, N. & Akyildiz, I. F., (2016) “Channel-aware routing and priority-aware multi-channel scheduling for WSN-based smart grid applications”, Journal of Network and Computer Applications, Vol. 71, pp50-58.

[12]  Mohemed, R. E., Saleh, A. I., Abdelrazzak, M. & Samra, A. S., (2017) “Energy-Efficient Routing Protocols for Solving Energy Hole Problem in Wireless Sensor Networks”, Computer Networks, Vol. 114, pp51-66.

[13]  Vimalarani, C., Subramanian, R. & Sivanandam, S. N., (2016) “An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network”, The Scientific World Journal, Vol. 2016.

[14]  Kuila, P. & Jana, P. K., (2014) “Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach”, Engineering Applications of Artificial Intelligence, Vol. 33, pp127–140.

[15] Balaji, S., Golden Julie, E., Rajaram, M. & Harold Robinson Y., (2016) “Fuzzy Based Particle Swarm Optimization Routing Technique for Load Balancing in Wireless Sensor Networks”, International Journal of Computer, Electrical, Automation, Control and Information Engineering, Vol. 10, No. 7,pp1418-1427.

[16]  Tang, J., Liu, A., Zhang, J., Xiong, N. N., Zeng, Z. & Wang, T., (2018) “A Trust-Based Secure Routing Scheme Using the Traceback Approach for Energy-Harvesting Wireless Sensor Networks”, Sensors, Vol. 18, No. 3, pp1-44.

[17]  Fardin Far, S. & Alaei, M., (2018) “A New Method to Reduce Energy Consumption in Manet Network Routing based on OLSR Protocol and Genetic Algorithm”, Journal of Advances in Computer Research, Vol. 9, No. 3, pp55-70.

[18]  Jacquet, P., Muhlethaler, P., Clausen, T., Laouiti, A., Qayyum, A. and Viennot, L., (2001) “Optimized Link State Routing Protocol for Ad Hoc Networks”, Proceedings IEEE International Multi Topic Conference (INMIC), 30-30 December, Lahore, Pakistan.

[19]  Li, L. & Li, D., (2018) “An Energy-Balanced Routing Protocol for a Wireless Sensor Network”, Journal of Sensors, Vol. 2018.

[20] Arthur, D. & Vassilvitskii, S., (2007) “k-Means plus plus: the advantages of careful seeding”, Proceedings of the 18th Annual Acm-Siam Symposium on Discrete Algorithms, New Orleans, LA, USA, 7-9 January, pp1027–1035.

[21] Heinzelman, W. B., Chandrakasan, A. P. & Balakrishnan, H., (2002) “An application-specific protocol architecture for wireless microsensor networks”, IEEE Transactions on Wireless Communications, Vol. 1, No. 4, pp660–670.

[22] Tewari, M. & Vaisla, K. S., (2014) “Performance study of SEP and DEC hierarchical clustering algorithm for heterogeneous WSN,” International Conference on Computational Intelligence and Communication Networks, Bhopal, India, 14-16 November, pp385–389.

[23]  Kamran Khan, M., Shiraz, M., Ghafoor, K. Z., Khan, S., Safaa Sadiq, A. & Ahmed, G., (2018) “EEMRP: Energy-Efficient Multistage Routing Protocol for Wireless Sensor Networks”, Wireless Communications and Mobile Computing, Vol. 2018.

[24] Tang, Y. J., Lee, C. W., Lin, M. H., Liu, B. H. & Tsai M. J., (2017) “Energy consumption reduction methods of geographic routing protocols with out-of-date location information in mobile ad hoc networks”, IEEE International Conference on Communications (ICC), 21-25 May, Paris, France.

[25]  Ogawa, E., Nakamura, S., Enokido, T. & Takizawa, M., (2017) “An Energy-aware One-to-one Routing Protocol in Wireless Ad-hoc Network, Proceeding of the 20th International Conference on Network-Based Information Systems (NBiS), 24–26 August, Toronto, Canada, pp1162-1168.

[26]  Ogawa, E., Nakamura, S., Enokido, T. and Takizawa, M., (2017) “A Low-energy Unicast Ad-hoc Routing Protocol in Wireless Networks”, Proceeding of the 12th International Conference on BroadBand Wireless Computing, Communication and Applications (BWCCA), 8–10 November, Barcelona, Spain, pp1162-1168.

[27] Ogawa, E., Nakamura, S. & Takizawa, M., (2017) “An Energy-saving Unicast Routing Protocol in Wireless Ad-hoc Network”, Proceeding of the 11th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), 10-12 July, Torino, Italy, pp1162-1168.

[28] gawa, E., Nakamura, S., Enokido, T. & Takizawa, M., (2018) “Unicast Routing Protocols to Reduce Electric Energy Consumption in Wireless Ad-Hoc Networks”, 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA), Krakow, Poland, 16-18 May.

[29] Patra, R. R. & Patra, P. K., (2011) “Analysis of k-Coverage in Wireless Sensor Networks”, International Journal of Advanced Computer Science and Applications, Vol. 2, No. 9, pp91-96.

AUTHOR

author-3

Gholamreza Farahani received his BSc degree in electrical engineering from Sharif University of Technology, Tehran, Iran, in 1998 and MSc and PhD degrees in electrical  engineering from Amirkabir University of Technology (Polytechnic), Tehran, Iran in 2000 and 2006 respectively. Currently, he is an assistant professor in the Institute of Electrical and Information Technology, Iranian Research Organization for Science and Technology (IROST), Iran. His research interest is computer networks especially routin.

Ensemble of Probabilistic Learning Networks For Iot Edge Intrusion Detection

 Tony Jan1and A.S.M Sajeev1

 1Melbourne Institute of Technology, Australia

 ABSTRACT

This paper proposes an intelligent and compact machine learning model for IoT intrusion detection using an ensemble of semi-parametric models with Ada boost. The proposed model provides an adequate realtime intrusion detection at an affordable computational complexity suitable for the IoT edge networks. The proposed model is evaluated against other comparable models using the benchmark data on IoT-IDS and shows comparable performance with reduced computations as required.

KEYWORDS

adaboosted ensemble learning, IoT edge security, machine learning for IoT

For More Details: http://aircconline.com/ijcnc/V10N6/10618cnc08.pdf

Volume Link: http://airccse.org/journal/ijc2018.html

REFERENCES

[1] Muhammad Umar Farooq, Muhammad Waseem, Anjum Khairi, and Sadia Mazhar, (2015) “A critical analysis on the security concerns of internet of things (IoT)”, International Journal of Computer Applications, Vol. 111, No. 7.

[2]  Manos Antonakakis, Tim April, Michael Bailey, Matt Bernhard, Elie Bursztein, Jaime Cochran, Zakir Durumeric, J Alex Halderman, Luca Invernizzi, Michalis Kallitsis, et al., (2017) Understanding the mirai botnet”, in USENIX Security Symposium.

[3] Antonio Brogi and Stefano Forti, (2017) “QoS-aware deployment of IoT applications through the fog”, IEEE Internet of Things Journal, Vol. 4, No. 5, pp. 1185–1192.

[4]  Farhoud Hosseinpour, Payam Vahdani Amoli, Juha Plosila, Timo Ham ¨ al¨ ainen, and Hannu ¨ Tenhunen, (2016) “An Intrusion Detection System for Fog Computing and IoT based Logistic Systems using a Smart Data Approach”, International Journal of Digital Content Technology and its Applications, Vol. 10.

[5] Bernard W Silverman, (2018) Density estimation for statistics and data analysis, Routledge.

[6]   Ron Kohavi, David H Wolpert, et al., (1996) “Bias plus variance decomposition for zero-one loss functions”, in ICML, Vol. 96, pp. 275–83.

[7] Yair Meidan, Michael Bohadana, Yael Mathov, Yisroel Mirsky, Dominik Breitenbacher, Asaf Shabtai, and Yuval Elovici, (2018) “N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders”, arXiv preprint arXiv:1805.03409.

[8] Alya Geogiana Buja, Shekh Faisal Abdul-Latip, and Rabiah Ahmad, (2018) “A Security Analysis of IoT Encryption: Side-channel Cube Attack on Simeck32/64, arXiv preprint arXiv:1808.03557.

[9]  Ryan Williams, Emma McMahon, Sagar Samtani, Mark Patton, and Hsinchun Chen, (2017) “Identifying vulnerabilities of consumer Internet of Things (IoT) devices: A scalable approach”, in Intelligence and Security Informatics (ISI), 2017 IEEE International Conference on, pp. 179–181.

[10]  Sudhi R Sinha and Youngchoon Park, (2017) Building an Effective IoT Ecosystem for Your Business, Springer.

[11]  Briana Arrington, LiEsa Barnett, Rahmira Rufus, and Albert Esterline, (2016) “Behavioral modeling intrusion detection system (bmids) using internet of things (iot) behavior-based anomaly detection via immunity-inspired algorithms”, in Computer Communication and Networks (ICCCN), 2016 25th International Conference on, pp. 1–6.

[12]  Otavio Carvalho, Manuel Garcia, Eduardo Roloff, Emmanuell Diaz Carre ´ no, and ˜Philippe OA Navaux, (2017) “IoT Workload Distribution Impact Between Edge and Cloud Computing in a Smart Grid Application”, in Latin American High Performance Computing Conference, pp. 203–217.

[13]  Eduardo Cuervo, Aruna Balasubramanian, Dae ki Cho, Alec Wolman, Stefan Saroiu, Ranveer Chandra, and Paramvir Bahl, (2010) “MAUI: making smartphones last longer with code offload, in Proceedings of the 8th international conference on Mobile systems, applications,and services, pp. 49–62.

[14]  Mark S Gordon, Davoud Anoushe Jamshidi, Scott A Mahlke, Zhuoqing Morley Mao, and Xu Chen, (2012) “COMET: Code Offload by Migrating Execution Transparently.”, in OSDI,Vol. 12, pp. 93–106.

[15]  Sokol Kosta, Andrius Aucinas, Pan Hui, Richard Mortier, and Xinwen Zhang, (2012) “Thinkair: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading”, in Infocom, 2012 Proceedings IEEE, pp. 945–953.

[16] Takayuki Nishio, Ryoichi Shinkuma, Tatsuro Takahashi, and Narayan B Mandayam, (2013) “Service-oriented heterogeneous resource sharing for optimizing service latency in mobile cloud”, in Proceedings of the first international workshop on Mobile cloud computing & networking, pp. 19–26.

[17] Elike Hodo, Xavier Bellekens, Andrew Hamilton, Pierre-Louis Dubouilh, Ephraim Iorkyase, Christos Tachtatzis, and Robert Atkinson, (2016) “Threat analysis of IoT networks using artificial neural network intrusion detection system”, in Networks, Computers and Communications (ISNCC), 2016 International Symposium on, pp. 1–6.

[18]  Nof Abuzainab, Walid Saad, Choong-Seon Hong, and H Vincent Poor, (2017) “Cognitive hierarchy theory for distributed resource allocation in the internet of things”, arXiv preprint arXiv:1703.07418.

[19]   Abdulla Amin Aburomman and Mamun Bin Ibne Reaz, (2016) “A novel SVM-kNN-PSO ensemble method for intrusion detection system”, Applied Soft Computing, Vol. 38, pp.360–372.

[20]   Wathiq Laftah Al-Yaseen, Zulaiha Ali Othman, and Mohd Zakree Ahmad Nazri, (2017) “Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system”, Expert Systems with Applications, Vol. 67, pp.296–303.

[21]  Milad Yousefi, Moslem Yousefi, Ricardo Poley Martins Ferreira, Joong Hoon Kim, and Flavio S Fogliatto, (2018) Chaotic genetic algorithm and Adaboost ensemble metamodeling approach for optimum resource planning in emergency departments”, Artificial intelligence in medicine, Vol. 84, pp. 23–33.

[22]  YANG Xinwu, M A Zhuang, and YUAN Shun, (2016) “Multi-class Adaboost Algorithm Based on the Adjusted Weak Classifier”, Journal of Electronics & Information Technology,Vol. 38, No. 2, pp. 373–380.

[23]  Anthony Zaknich, (1998) “Introduction to the modified probabilistic neural network for general signal processing applications”, IEEE Transactions on Signal Processing, Vol. 46,No. 7, pp. 1980–1990.

[24]  Elike Hodo, Xavier Bellekens, Andrew Hamilton, Pierre-Louis Dubouilh, Ephraim Iorkyase,Christos Tachtatzis, and Robert Atkinson, (2016) “Threat analysis of IoT networks using artificial neural network intrusion detection system”, in Networks, Computers and Communications (ISNCC), 2016 International Symposium on, pp. 1–6.

Enhancing and Measuring the Performance in Software Defined Networking

1Md. Alam Hossain, 1Mohammad Nowsin Amin Sheikh,2,*Shawon S. M. Rahman,Sujan Biswas, and 1Md. Ariful Islam Arman

1Dept. of Computer Science & Engineering, Jessore University of Science andTechnology, Jessore, Bangladesh

2Associate Professor, Dept. of Computer Science & Engineering, University of HawaiiHilo, 200 W. Kawili Street, Hilo, HI 96720, USA

ABSTRACT

Software Defined Networking (SDN) is a challenging chapter in today’s networking era. It is a network design approach that engages the framework to be controlled or ‘altered’ adroitly and halfway using programming applications. SDN is a serious advancement that assures to provide a better strategy than displaying the Quality of Service (QoS) approach in the present correspondence frameworks. SDN etymologically changes the lead and convenience of system instruments using the single high state program. It separates the system control and sending functions, empowering the network control to end up specifically. It provides more functionality and more flexibility than the traditional networks. A network administrator can easily shape the traffic without touching any individual switches and services which are needed in a network. The main technology for implementing SDN is a separation of data plane and control plane, network virtualization through programmability. The total amount of time in which user can respond is called response time. Throughput is known as how fast a network can send data. In this paper, we have design a network through which we have measured the Response Time and Throughput comparing with the Real-time Online Interactive Applications (ROIA), Multiple Packet Scheduler, and NOX.

KEYWORDS

Software Defined Networking, SDN, Quality of Service, QoS, Real-time Online Interactive Application, ROIA, Network Operating System, NOX, CES, MPLSTE, Switch Capacity, Number of Queues Impact, QoE Evaluation, Bandwidth Isolation

For More Details : http://aircconline.com/ijcnc/V10N5/10518cnc02.pdf

 Volume  Link : http://airccse.org/journal/ijc2018.html

REFERENCES

[1] “Improving QoS in Real-Time Internet Applications: From Best-Effort to Software-Defined Networks – IEEE Xplore Document.”10 April 2014.

[2] “Control of Multiple Packet Schedulers for Improving QoS on OpenFlow/SDN Networking – IEEE Xplore Document.” 12 December 2013.

[3] Mudit Saxena, and Dr. Rakesh Kumar.”A Recent Trends in Software Defined Networking (SDN) Security.” International Conference on Computing for Sustainable Global Development (INDIACom).on 2016 IEEE.

[4]     Natasha Gude et al., “NOX: Towards an Operating System for Networks,” editorial note submitted to CCR.

[5]     Arsalan Tavakoli et al, “Applying NOX to the Datacenter,” in Proc. Of SIGCOMM Hotnet 2009.

[6] Dimitri Staessens et al., “Software Defined Networking: Meeting Carrier Grade Requirements,” in Proc. of IEEE Workshop on Local & Metropolitan Area Networks (LANMAN), 2011.

[7] P. Georgopoulos, Y. Elkhatib, M. Broadbent et al., “Towards network wide QoE fairness using OpenFlow-assisted adaptive video streaming,” in Proc. of the 2013 ACM SIGCOMM Workshop on Future Human- Centric Multimedia Networking (FhMN 2013), Hong Kong, China, 2013, pp. 15–20.

[8] T. Zinner, M. Jarschel, A. Blenk et al., “Dynamic application-aware resource management using software-defined networking: implementation prospects and challenges,” in Proc. of the 2014 IEEE Network Operations and Management Symposium (NOMS ’14), Krakow, Poland, 2014, pp. 1–6.

[9]     Lazaris, D. Tahara, X. Huang et al., “Tango: simplifying SDN control with automatic switch property inference, abstraction, and optimization,” in Proc. of the 10th ACM International on Conference on emerging Networking Experiments and Technologies (CoNEXT), Sydney, Australia, 2014, pp. 199– 212.

[10] M. Kuzniar, P. Peresini, and D. Kostic, “What you need to know about SDN control and data planes,” EPFL, Lausanne, Switzerland, Tech. Rep. EPFL-REPORT-199497, 2014.

[11] V. Mann, A. Vishnoi, A. Iyer et al., “VMPatrol: dynamic and automated QoS for virtual machine migrations,” in Proc. of the 8th International Conference on Network and Service Management (CNSM), Las Vegas, USA, 2012, pp. 174–178.

[12] Z. Bozakov and A. Rizk, “Taming SDN controllers in heterogeneous hardware environments,” in Proc. of Second European Workshop on Software Defined Networks (EWSDN), Berlin, Germany, 2013, pp. 50 – 55.

[13] M. Kuzniar, P. Peresini, and D. Kostic, “What you need to know about sdn flow tables,” in Passive and Active Measurement, ser. Lecture Notes in Computer Science, J. Mirkovic and Y. Liu, Eds. Springer International Publishing, 2015, vol. 8995, pp. 347–359.

[14] P. M. Mohan, D. M. Divakaran, and M. Gurusamy, “Performance study of TCP flows with QoSsupported OpenFlow in data center networks,” in Proc. of the 19th IEEE International Conference on Networks (ICON), Singapore, Singapore, 2013, pp. 1–6

[15] Nguyen-Ngoc, S. Lange, S. Gebert et al., “Investigating isolation between virtual networks in case of congestion for a Pronto 3290 switch,” in Proc. of the Workshop on Software-Defined Networking and Network Function Virtualization for Flexible Network Management (SDNFlex 2015), Cottbus, Germany, 2015.

[16] Bari, M.F., Chowdhury, S.R., Ahmed R., Boutaba, R.: PolicyCop: an autonomic QoS policy enforcement framework for software defined networks. In: IEEE SDN for Future Networks and Services, Trento, Italy, pp. 1–7, November 2013.

[17] Egilmez, H.E., Dane, S.T., Bagci, K.T., Tekalp, A. M.: OpenQoS: an openflow controller design for multimedia delivery with end-to-end Quality of Service over Software-Defined Networks. In: Proceedings of the Signal and Information Processing Association Annual Summit and Conference, Hollywood, California, US, pp. 1–8, December 2012.

[18] Guo, J., Fangming, L., Haowen, T., Yingnan, L., Hai, J., John, L.: Falloc: fair network bandwidth allocation in IaaS datacenters via a bargaining game approach. In: Proceedings of the ICNP, Gotingen, Germany, pp. 1–10, October 2013.

[19] Benson, T., Akella, A., Shaikh, A., Sahu, S.: CloudNaaS: a cloud networking platform for enterprise applications. In: Proceedings of the 2nd ACM Symposium on Cloud Computing, Cascais, Portugal (2011).

[20] Jain, S., et al.: B4: Experience with a globally-deployed software defined WAN. ACM SIGCOMM Comput. Commun. Rev. 43(4), 3–14 (2013).

[21]   Kim, W., et al.: Automated and scalable QoS control for network convergence. In: Proceedings of the INM/WREN, San Jose, California, US (2010).

[22]   M. Betts, S. Fratini, N. Davis, R. Dolin and others, “SDN Architecture”. Open Networking Foundation ONF SDN ARCH, Issue 1, June, 2014.

[23]   M. Joselli et al., “An Architeture with Automatic Load Balancing for Real-Time Simulation and Visualization Systems,” Journal of Computational Interdisciplinary Sciences, vol. 1, no. 3, pp. 207– 224, 2010.

[24] Bert Hubert, Thomas Graf, Gregory Maxwell, Remco Van Mook, Martijn Van Oosterhout, Paul B. Schroeder, Jasper Spaans, and Pedro Larroy. Linux Advanced Routing & Traffic Control HOWTO. Linux Advanced Routing & Traffic Control, http://lartc.org/, April 2004.

[25] Paul E McKenney. Stochastic fairness queueing. In INFOCOM’90. Ninth Annual Joint Conference of the IEEE Computer and Communication Societies.’The Multiple Facets of Integration’. Proceedings. IEEE, pages733–740. IEEE, 1990.

[26] “On Scalability of Software-Defined Networking – IEEE Xplore Document.” 14 February 2013.

[27] Alexander So.”Survey on Recent Software-Defined Network Cross-Layer Designs.” https://www.researchgate.net/publication/311953252_Survey_on_Recent_Software.Defined_Network Cross-Layer_Designs on December 2016.

[28] Cisco 2016. Unicast flooding in switched campus networks; http:// www. Cisco.com/c/n/us/support/docs/switches/catalyst/6000-series-switches/23563-143.html.

[29]   ONF.OpenFlow table type patterns, Open Network-ing Foundation, Tech. Rep.Available from: https://www.opennetworking.org/images/stories/downloads/sdnresources/onfspecifications/openflow/ OpenFlow,[Accessed on: March 9, 2018].

[30] Haleplidis E, Denazis S, Pentikousis K, Denazis S,Salim JH, Meyer D, Koufopavlou O.SDN layersand architecture terminology, Internet draft, Internet engineering task force. Available from: https://www.ietf.org/id/draft-irtfsdnrg-layer-terminology-02.txt,%5BAccessed on: February 20, 2018].

[31] ON.LAB. ONOS: Open Network Operating System. In ONS, 2017.

[32] H. Howard, D. Malkhi, and A. Spiegelman. Flexible Paxos: Quorum intersection revisited. CoRR, abs/1608.06696, 2016.

[33] Loukaka, Alain and Rahman, Shawon; “Discovering New Cyber Protection Approaches From a Security Professional Prospective”; International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.4, July 2017

[34] Al-Mamun, Abdullah, Rahman, Shawon and et al;“ Security Analysis of AES and Enhancing its Security by Modifying S-Box with an Additional Byte ”; International Journal of Computer Networks & Communications (IJCNC), Vol.9, No.2, March 2017

[35] Opala, Omondi John; Rahman, Shawon; and Alelaiwi, Abdulhameed; “The Influence of Information Security on the Adoption of Cloud computing: An Exploratory Analysis”, International Journal of Computer Networks & Communications (IJCNC), Vol.7, No.4, July 2015

[36] Halton, Michael and Rahman, Syed (Shawon); “The Top 10 Best Cloud-Security Practices in NextGeneration Networking“; International Journal of Communication Networks and Distributed Systems (IJCNDS), Vol. 8, Nos. ½, 2012, Pages:70-84

[37] Schuett, Maria and Rahman, Syed (Shawon); “Information Security Synthesis in Online Universities”; International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.5, Sep 2011

AUTHORS

Md. Alam Hossain is working as an Assistant Professor at the Department of Computer Science & Engineering in Jessore University of Science & Technology, Bangladesh. He completed his B.Sc and M.Sc (Thesis) in Computer Science & Engineering from Islamic University, Bangladesh. Currently, he is pursuing Ph.D. on Cloud Computing Security.

Mohammad Nowsin Amin Sheikh is working as an Assistant Professor at the Department of Computer Science & Engineering in Jessore University of Science & Technology (JUST), Jessore, Bangladesh. He completed his B.Sc. (Engg.) in Computer Science & Engineering from Jessore University of Science & Technology (JUST), Jessore, Bangladesh.

Dr. Shawon S. M. Rahman is an Associate professor in the Department of Computer Science and Engineering at the University of Hawaii-Hilo. His research interests include software engineering education, information assurance and security, web accessibility, cloud computing, and software testing andquality assurance. He has published over 100 peer-reviewed papers. He is an active member of manyprofessional organizations including IEEE, ACM, ASEE, ASQ, and UPE.

Sujan Biswas completed his B.Sc in Computer Science & Engineering from Jessore University of Science & Technology, Bangladesh.

Md. Ariful Islam Arman completed his B.Sc in Computer Science & Engineering from Jessore University of Science & Technology, Bangladesh.

A Future Mobile Packet Core Network Based on Ip-In-Ip Protocol

Mohammad Al Shinwan1and Kim Chul-Soo2

1Faculty of Computer Science and Informatics, department of Mobile Computing, Amman Arab University, Amman, Jordan.

2Department of Computer Engineering, Inje University, Gimhae, Republic of Korea.

ABSTRACT

The current Evolved Packet Core (EPC) 4th generation (4G) mobile network architecture features complicated control plane protocols and requires expensive equipment. Data delivery in the mobile packet core is performed based on a centralized mobility anchor between eNode B (eNB) elements and the network gateways. The mobility anchor is performed based on General Packet Radio Service tunnelling protocol (GTP), which has numerous drawbacks, including high tunnelling overhead and suboptimal routing between mobile devices on the same network. To address these challenges, here we describe new mobile core architecture for future mobile networks. The proposed scheme is based on IP encapsulated within IP (IP-in-IP) for mobility management and data delivery. In this scheme, the core network functions via layer 3 switching (L3S), and data delivery is implemented based on IP-in-IP routing, thus eliminating the GTP tunnelling protocol. For handover between eNB elements located near to one another, we propose the creation of a tunnel that maintains data delivery to mobile devices until the new eNB element updates the route with the gateway, which prevents data packet loss during handover. For this, we propose Generic Routing Encapsulation (GRE) tunnelling protocol. We describe the results of numerical analyses and simulation results showing that the proposed network core architecture provides superior performance compared with the current 4G architecture in terms of handover delay, tunnelling overhead and total transmission delay.

KEYWORDS

5G network, mobile core network, IP-in-IP, GRE

For More Details :  http://aircconline.com/ijcnc/V10N5/10518cnc05.pdf

Volume Link : http://airccse.org/journal/ijc2018.html

REFERENCES

[1]   Cisco, (2016) “Visual Networking Index: Global Mobile Data Traffic Forecast Update”, 2015 – 2020,White paper.

[2] Ericsson, Huawei and Qualcomm, (2015) “The Road to 5G: Drivers, Applications, Requirements and Technical Development”, Technical Report.

[3]   Shinwan, M.A. and Chul-Soo, K. (2017) “Enhanced Mobile Packet Core Network Scheme for Next-Generation Mobile Communication Systems. International Journal of Electronics Communication and Computer Engineering (IJECCE), 8, 56-61.

[4]   3GPP TS 23.002, (2016) “Technical Specification Group Services and System Aspects; Network architecture”, Technical Report, Rel-13 Ver. 13.5.0.

[5] Seite,P., P. Bertin, (2010) “Dynamic Mobility Anchoring”, IETF. https://tools.ietf.org/html/draft-seite-netext-dma-00

[6]   D. Liu, P. Seite, H. Yokota and J. Korhonen, (2014) “Requirements for distributed mobility management”, IETF RFC 7333, https://doi.org/10.17487/rfc7333.

[7]   F. Rebecchi, M. Dias de Amorim, V. Conan, A. Passarella, R. Bruno and M. Conti, (2015)” Data of loading Techniques in Cellular Networks: A Survey”. IEEE Communications Surveys and Tutorials,vol. 17, no. 2, pp. 580-603. https://doi.org/10.1109/comst.2014.2369742.

[8]  K. Daoud, P. Herbelin and N. Crespi. (2015) UFA: Ultra Flat Architecture for high bitrate services in mobile networks. IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications, pp.1-6. https://doi.org/10.1109/pimrc.2008.4699577.

[9]  Gohar, M., Choi, JG. & Koh, SJ, (2016) “TRILL-Based Mobile Packet Core Network for 5G Mobile Communication Systems”. Wireless Pers Commun. volume 87, issue 1, (p.p 125 – 144),https://doi.org/10.1007/s11277-015-3035-5.

[10]  Heeyoung Jung, Moneeb Gohar and Seok-Joo Koh, (2014) “RB-core: Routing bridge-based 5G mobile core network”, International Conference on Information and Communication Technology Convergence (ICTC), (pp.223-228), https://doi.org/10.1109/ictc.2014.6983122.

[11]   Cisco, “Catalyst 2948G-L3 Switch High-Performance Layer 3 Switching”, Technical Report. http://www.cisco.com/en/US/products/hw/switches/ps606/products_data_sheet09186a008009267f.html

[12] Perkins, Charles, “IP encapsulation within IP”, (2003) IETF RFC 2003,https://doi.org/10.17487/rfc2003.

[13]   3gpp.TS 23.401, (2016) “General Packet Radio Service (GPRS) enhancements for Evolved Universal Terrestrial Radio Access Network (EUTRAN) access”, Rel-13, Ver.13.6.1.

[14]   S. Hanks, T. Li, D. Farinacci, P. Traina. (1994) “Generic routing encapsulation (GRE)”, IETF RFC 2784, https://doi.org/10.17487/rfc1701.

[15]  Nuutti Varis, Jukka Manner and Johanna Heinonen., (2011) “A layer-2 approach for mobility and transport in the mobile backhaul”, ITS Telecommunications (ITST), 2011 11th International Conference, (pp.268-273), https://doi.org/10.1109/itst.2011.6060066.

[16]   C. Makaya and S. Pierre. (2008) An Analytical Framework for Performance Evaluation of IPv6-Based Mobility Management Protocols. IEEE Transactions on Wireless Communications, vol. 7, no.3, pp. 972-983. https://doi.org/10.1109/twc.2008.060725.

[17]   Ns-3 Open Source Network Simulator. https://www.nsnam.org/

[18]   M. A. Shinwan, T.-D. Huy, and K. Chul-Soo, (2017) “A Flat Mobile Core Network for Evolved Packet Core Based SAE Mobile Networks,” Journal of Computer and Communications, vol. 05, no.05, pp. 62-73.

[19]  B. Davaasambuu, F. Semaganga, and T. Sato, (2015) “Adaptive Handover Hysteresis and Call Admission Control for Mobile Relay Nodes,” International journal of Computer Networks & Communications, vol. 7, no. 6, pp. 87–98.

AUTHORS

author-4

Mohammad Al Shinwan, received his B.S degree in Computer Science from Al al-Bayt university, Jordan, in 2009 and the master degree in 2013 from the institute of Mathematical and Computer Science, University of Sindh in Pakistan. He received a Ph.D. in Computer Networks from Inje University, Korea. He is currently an assistant professor in the Faculty of Computer Science and Informatics, Amman Arab University, Jordan. His current research interests include mobility management, network management and OAM (Operation, Administration and Maintenance) for future mobile network.

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Chul-Soo Kim is a professor in the School of Computer Engineering of Inje University in Gimhae, Korea. He received Ph.D. from the Pusan National University (Pusan, Korea) and worked for ETRI (Electronics and Telecommunication research Institute) from 1985 – 2000 as senior researcher for developing TDX exchange. Aside from the involvement in various national and international projects, his primary research interests include network protocols, traffic management, OAM issue, and NGN charging. He is a member of ITU-T SG3, SG11, SG13 and a Rapporteur of ATM Lite from 1998 – 2002, and CEO in WIZNET from 2000 – 2001. He is currently the chairperson of BcN Reference Model in Korea, and a Rapporteur of ITU-T SG3 NGN Charging.

Performances of Ad Hoc Networks under Deterministic And Probabilistic Channel Conditions: Cases For Single Path And Multipath Routing Protocols

Mohammed Tarique and Rumana Islam

Department of Electrical Engineering, Ajman University-Fujairah CampusP.O. Box 2202, Fujairah, United Arab Emirates

ABSTRACT

 Deterministic channel models have been widely used in simulation and modeling of ad hoc network for a long time. But, deterministic channel models are too simple to represent a real-world ad hoc network scenario. Recently, random channel models have drawn considerable attention of the researchers in this field. The results presented in the literature show that random channel models have a grave impact on the performance of an ad hoc network. A comprehensive investigation on this issue is yet to be available in the literature. In this investigation, we consider both deterministic and random channel models to investigate their effects on ad hoc networks. We consider two different types of routing protocols namely single path and multipath routing protocols. We choose Destination Sequence Distance Vector (DSDV), Dynamic Source Routing Protocol (DSR), and Ad-hoc On-Demand Distance Vector (AODV) as the single path routing protocols. On the other hand, we choose Ad-hoc On-Demand Multiple Path Distance Vector (AOMDV) as the multipath routing protocol. The results show that some single path routing protocol can outperform multipath routing protocol under both deterministic and random channel conditions. These results surprisingly contradict the popular claim that multipath routing protocol always outperforms single path routing protocol. A guideline for choosing an appropriate routing protocol for adhoc network has also been provided in this work.

KEYWORDS

Network Protocols, Single Path, Multipath, DSR, AODV, DSDV, AOMDV, Random Channel, Deterministic Channels, Network Performances

For More Details : http://aircconline.com/ijcnc/V10N4/10418cnc01.pdf

Volume Link : http://airccse.org/journal/ijc2018.html

REFERENCES

[1]  A.J. Goldsmith and S.B. Wicker, “Design Challenges for Energy-Constrained Ad hoc Wireless Networks”, IEEE Wireless Communication, Vol. 9, No. 4, August 2002, pp. 8-27

[2]  C.E. Perkins and P. Bhagwat, “Highly dynamic destination-sequence distance-vector routing (DSDV) for mobile computers” Proceedings of ACM SIGCOMM London, UK, August 1994, pp. 234-244

[3]   J. Broch, D. B. Johnson, and D. A. Maltz, “The Dynamic Source Routing Protocol for Mobile Ad Hoc Networks”, IETF Internet-Draft, draft-ietf-manet-dsr-00.txt March, 1998

[4]   C.E. Perkins, “Ad Hoc On Demand Distance Vector (AODV) routing”, Internet-Draft, draft-ietfmanet-aodv-00.txt November 1997

[5]  Das SR, Perkins CE, Royer EM, and Marina MK, “Performance comparison of two on-demand routing protocols for ad hoc networks”, IEEE Personal Communications Magazine, Vol. 8, No.1, February 2001, pp.16-28

[6]  Tseng Y-C, Ni S-Y, Chen Y-S, and Sheu J-P, “The broadcast storm problem in a mobile ad hoc network”, Wireless Networks, Vol. 8, No. 2–3, March 2002, pp.153–167.

[7]   Mohammed Tarique, Kemal. E. Tepe, SasanAdibi, and ShervinErfani, “ Survey of multipath routing protocols for mobile ad hoc networks”, Journal of Networks and Computer Application, Vol. 32, No.6, November 2009, pp. 1125-1143.

[8]  Theodore S. Rappaport , Wireless Communication Principles and Practices”, 2nd Edition, Prentice Hall, pp. 101-107

[9]  Y.-C. Cheng and I.G. Robertazzi, “Critical connectivity phenomena in multihop radio Models”, IEEE Transaction on Communication, Vol. 37, No.7, July 1989, pp. 770- 777

[10] P. Piret, “ On the connectivity of radio networks”, IEEE Transaction on Information Theory, Vol. 37, No. 5, September 1991, pp. 1490-1492

[11] P. Gupta and P.R. Kumar, “Critical power for asymptotic connectivity in wireless networks”,Proceedings of IEEE Conference, Contl, December 1998, pp. 1106-110

[12] P. Santi and D.M. Blough, “The critical transmitting range for connectivity in sparse wireless ad hoc networks”, IEEE Transaction on Mobile Computing, Vol. 2, No. 1, March 2003, pp.25-39

[13] P. Santi, M. Blough, and F. Vainstein, “A probabilistic analysis for the radio range assignment problem in ad hoc networks”, Proceedings of ACM International Symposium on MobileAd Hoc Network and Computers (MobiHoc), Long Beach, USA, October 2001

[14] C. Bettstetter, “ On the minimum node degree and connectivity of a wireless multihopnetwork”, Proceedings of ACM International Symposium on Mobile Ad hoc Network and Computing MobiHoc), Lausanne, Switzerland, June 2002

[15] C. Betstetter, “On the connectivity of ad hoc networks”, The Computer Journal, Vol. 47, N0.4,  July 2004, Oxford University Press, pp. 432-447

[16] O. Douse, P. Thiran, and M. Hasler, “Connectivity in ad hoc and hybrid networks”, Proceedings of IEEE Infocom, New York, USA, June 2002

[17] Bau Hua Liu, Brian P. Otis, SubashChalla, Paul Axon, Chun Tung Chou, Sanjay K. Jha, “The impact of fading and shadowing on the network performance of wireless sensor networks”, International Journal of Sensor Networks, Vol. 3, No. 4, June 2008, pp. 211-223

[18] Christian Bettsetter and Christian Hartmann, “Connectivity of Wireless Multihop Networks in aShadow Fading Environment”, Proceedings of AM International Workshop onModelling, Analysis ,and Simulation Of Wireless and Mobile System, San Diego, USA,September 2003

[19] Studei, P., Chinellato, O. and Alonso, G., “Connectivity in the presence of shadowing in 802.11  ad hoc networks”, In the Proceedings of IEEE Wireless Communication and Networking (WCNC),March 2005, Vol.4, pp. 13-17

[20] Md. Anwar Hossain, Mohammed Tarique, and Rumana Islam, “ Shadowing Effects on Routing Protocol of Multihop Ad Hoc Networks”, International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC), Vol. 1, No. 1, March 2010, pp. 12-28

[21] DrupadDebnath, Chowdhury Akram, Rumana Islam et. al. “Minimizing Shadowing Effects on Mobile Ad hoc Networks”, Journal of Selected Areas on Telecommunication (JSAT), October 2011, pp. 46-51

[22] Daniele Miorandi,” The Impact of Channel Randomness on Coverage and Connectivity of Ad Hoc and Sensor Networks”, IEEE Transaction on Wireless Communication, Vol. 7, No. 3, March 2008, pp. 1062 – 1072

[23]  Nagesh K. N ; Satyanarayana D, NageshPoojary, and ChandrashekarRamiah, “ An analytical expression for k-connectivity probability of wireless ad hoc networks in presence of channel randomness”, Proceedings of IEEE International Conference on Wireless Information Technology and Systems, November 11-16, 2012, Maui, Hi, USA, pp.

[24]  D. Miorandi ; E. Altman, “ Coverage and connectivity of Ad hoc networks in presence of channel Randomness”, Proceedings of the 24th Annual Joint Conference of the IEEE Computerand Communication Societies INFOCOM 2005, March 13-17, 2005, pp. 491-502

[25] DavideDardari,” On the Connected Nodes Position Distribution in Ad HocWireless Networks with Statistical Channel Models”, Proceedings of the IEEEConference on Communication, June 24-28, Glasgow, UK, pp. 4741-4747

[26] Mahesh K. Marina1and Samir R. Das, “Ad hoc on-demand multipath distance vector routing” , Wireless Communication and Mobile Computing, Vol. 6, 2006, pp. 969-988

[27] Dimitri Bertsekas, Robert G. Gallager, Data Networks”, Second Edition, Prentice Hall, pp. 396-400

[28] Beharouz A. Forouzan, “Communication and Data Networks”, 4th Edition, McGraw Hill, New York, pp. 665-670

[29] Leonard E. Miller, “Distribution of Link Distances in a Wireless Network”, Journal of Research of the National Institute of Standards and Technology, Vol. 106, No. 2, March-April, 2001, pp.401-412

[30]  The Network Simulator (NS-2) available at https://www.isi.edu/nsnam/ns/

A Security Analysis of Iot Encryption: Sidechannel Cube Attack on Simeck32/64

Alya Geogiana Buja1,2, Shekh Faisal Abdul-Latip1and Rabiah Ahmad1

1 INSFORNET, Center for Advanced Computing Technology, Universiti TeknikalMalaysia Melaka, Hang Tuah Jaya, Durian Tunggal, 76100 Melaka, Malaysia

2Universiti Teknologi MARA, Shah Alam, 40450 Selangor, Malaysia

 ABSTRACT

Simeck, a lightweight block cipher has been proposed to be one of the encryption that can be employed in the Internet of Things (IoT) applications. Therefore, this paper presents the security of the Simeck32/64 block cipher against side-channel cube attack. We exhibit our attack against Simeck32/64 using the Hamming weight leakage assumption to extract linearly independent equations in key bits. We have been able to find 32 linearly independent equations in 32 key variables by only considering the second bit from the LSB of the Hamming weight leakage of the internal state on the fourth round of the cipher. This enables our attack to improve previous attacks on Simeck32/64 within side-channel attack model with better time and data complexity of 235 and 211.29 respectively.

KEYWORDS

Block Cipher, IoT, Lightweight Encryption, Security Analysis, Simeck

For More Details : http://aircconline.com/ijcnc/V10N4/10418cnc06.pdf

Volume Link: http://airccse.org/journal/ijc2018.html

REFERENCES

[1] G. Yang, B. Zhu, V. Suder, M. Aagaard, G. Gong, The Simeck Family of Lightweight Block Ciphers,CHES 2015. LNCS 9293 (2015) 307-329.

[2]  R. Beaulieu, D. Shors, J. Smith, S. Treatman- Clark, B. Weeks, L. Wingers, The SIMON and SPECK Families of Lightweight Block Ciphers Cryptology ePrint Archive, Report 2013/404.

[3]   V. Nalla, R. Sahu, V. Saraswat, Differential Fault Attack on SIMECK, Proceedings of the Third Workshop on Cryptography and Security in Computing Systems (2016) 45-48.

[4]  I. Dinur, A. Shamir, Cube Attacks on Tweakable Black Box Polynomials, EUROCRYPT 2009.LNCS 5479 (2009) 278-299.

[5]  J. Aumasson, I. Dinur, M. Meier, A. Shamir, Cube Testers and Key Recovery Attacks on Reducedround MD6 and Trivium, FSE 2009. LNCS 5665 (2009) 307-329.

[6]   Z. Ahmadian, S. Rasoolzadeh, M. Salmasizadeh, M. Aref, Automated Dynamic Cube Attack on Block Ciphers: Cryptanalysis of SIMON and KATAN, Cryptology ePrint Archive, 2015/040.

[7] S.F. Abdul-Latip, M. Reyhanitabar, W. Susilo, J. Seberry, Extended Cubes: Enhancing the Cube Attack by Extracting Low-Degree Non-Linear Equations, ASIACCS 2011 (2011) 296-305.

[8]    L. Yang, M. Wang, S. Qiao, Side Channel Cube Attack on PRESENT, CANS 2009. LNCS 5888 (2009) 379-391.

[9]   S.F. Abdul-Latip, M. Reyhanitabar, W. Susilo, J. Seberry, On the Security of NOEKEON against Side Channel Cube Attacks, ISPEC 2010, LNCS 6047 (2010) 45-55.

[10]   C. Canniere, O. Dunkelman, M. Knezevic, KATAN and KTANTAN-A Family of Small and Efficient Hardware-Oriented Block Ciphers, CHES 2009, LNCS 5747 (2008) 272-288.

[11]   F. Abed, E. List, S. Lucks, J.Wenzel, Differential and Linear Cryptanalysis of Reduced-round Simon, http://eprint.iacr.org/2013/526. (2013).

[12]   H. A. Alkhzaimi, M. M. Lauridsen, Cryptanalysis of the SIMON Family of Block Ciphers, https://eprint.iacr.org/2013/543.pdf. (2013).

[13]   J. Daemen, V. Rijmen, AES Proposal: Rijndael, The First Advanced Encryption Standard Candidate Conference.

 [14]  R. Anderson, B. Biham, L. Knudsen, Serpent: A Proposal for the Advanced Encryption Standard, The First Advanced Encryption Standard Candidate Conference.

[15]   X. Zhao, S. Guo, F. Zhang, T. Wang, Z. Shi, H. Liu, K. Ji, H. J, Efficient Hamming Weight-based Side-channel Cube Attacks on PRESENT, Journal of Systems and Software 86(3) (2013) 728-743.

[16]   K. Zhang, J. Guan, B. Hu, D. Lin, Security Evaluation on Simeck against Zero Correlation Linear Cryptanalysis, Cryptology ePrint Archive, Report 2015/911.

[17]  K. Qiao, L. Hu, S. Sun, Differential Security Evaluation of Simeck with Dynamic Key-guessing Techniques, Cryptology ePrint Archive, Report 2015/902.

[18]   S. Kolbl, A. Roy, A Brief Comparison of SIMON and Simeck, Cryptology ePrint Archive, Report 2015/706.

[19]   F. Zhang, S. Guo, X. Zhao, T. Wang, J. Yang, F.-X. Standaert, D. Gu, A Framework for the Analysis and Evaluation of Algebraic Fault Attacks on Lightweight Block Ciphers,10.1109/TIFS.2016.2516905, (2016).

[20]   L. Qin, H. Chen, Linear Hull Attack on Round-reduced Simeck with Dynamic Key-guessing Techniques, Cryptology ePrint Archive, Report 2016/066.

[21]  Z. Xiang, W. Zhang, Z. Bao, D. Lin, Applying MILP Method to Searching Integral Distinguishers based on Division Property for 6 Lightweight Block Ciphers, http://eprint.iacr.org/2016/857, (2016).

[22] N. Bagheri, Linear Cryptanalysis of Reduced-round Simeck Variant, Cryptology ePrint Archive, Report 2015/716.

[23]   M. Blum, M. Luby, R. Rubinfield, Self-Testing/Correcting with Application to Numerical Problems, STOC (1990) 73-83.

[24]   I. Dinur, A. Shamir, Side Channel Cube Attacks on Block Ciphers, Cryptology ePrint Archive, Report 2009/127.

[25]   G. Bard, N. Courtois, J. Nakahara, P. Sepehrdad, B. Zhang, Algebraic, AIDA/Cube and Side-Channel Analysis of KATAN Family of Block Ciphers, INDOCRYPT.

[26]   S. Madakam, R. Ramaswamy and S. Tripathi, S., 2015. Internet of Things (IoT): A literature review. Journal of Computer and Communications, 3(05), pp.164.

[27]  A. Haroon, M. A. Shah, Y. Asim, W. Naeem, M. Kamran, Q. Javaid, 2016. Constraints in the IoT: The World in 2020 and Beyond, International Journal of Advanced Computer Science and Applications, Vol. 7, pp. 252-271.

AUTHORS

author-6

Alya Geogiana Buja is a Ph.D. student at the Faculty of Information Technology and Communication, Universiti Teknikal Malaysia Melaka, Malaysia. Her research interests include information and network security. She involves actively in giving seminar and talk about information and security.

author-7Shekh Faisal Abdul-Latip is a Senior Lecturer at the Faculty of Information Technology and Communication, Universiti Teknikal Malaysia Melaka, Malaysia. He received PhD degree in 2012 from the University of Wollongong, Australia, in the field of Symmetric-key Cryptography. Currently he is an executive committee member of Malaysian Society for Cryptology Research (MSCR) – a non-profit organization that promotes new ideas and activities in cryptology related areas in Malaysia. His research focuses on Cryptology (i.e. designing and breaking secret codes) and Information Security.

author-8

Rabiah Ahmad is a Professor at the Faculty of Information Technology and Communication, Universiti Teknikal Malaysia Melaka, Malaysia. She received her PhD in Information Studies (health informatics) from the University of Sheffield, UK, and M.Sc. (information security) from the Royal Holloway University of London, UK. Her research interests include healthcare system security and information security architecture. She has delivered papers at various health informatics and information security conferences at national as well as international levels. She has also published papers in accredited national/international journals. Besides that, she also serves as a reviewer for various conferences and journals.

Deployment of Intrusion Prevention System on Multi-Core Processor Based Security Hardware

Swetha K V1 and Ravi Dara2

1Department of Computer Science & Engineering, CMR Institute of Technology,Bangalore, India

2Nevis Networks(I) Pvt.Ltd., Pune, India

ABSTRACT

After tightening up network perimeter for dealing with external threats, organizations have woken up to the threats from inside Local Area Networks (LAN) over the past several years. It is thus important to design and implement LAN security strategies in order to secure assets on LAN by filtering traffic and thereby protecting them from malicious access and insider attacks. Banking Financial Services and Insurance (BFSI) industry is one such segment that faces increased risks and security challenges. The typical architecture of this segment includes several thousands of users connecting from various branches over Wide Area Network (WAN) links crossing national and international boundaries with varying network speed to access data center resources. The objective of this work is to deploy LAN security solution to protect the data center located at headquarters from the end user machines. A LAN security solution should ideally provide Network Access Control (NAC) along with cleaning (securing) the traffic going through it. Traffic cleaning itself includes various features like firewall, intrusion detection/prevention, traffic anomaly detection, validation of asset ownership etc. LANenforcer (LE) is a device deployed in front of the data center such that the traffic from end-user machines necessarily passes through it so that it can enforce security. The goal of this system is to enhance the security features of a LANenforcer security system with Intrusion Prevention System (IPS) to enable it to detect and prevent malicious network activities. IPS is plugged into the packet path based on the configuration in such a way that the entire traffic passes through the IPS on LE.

KEYWORDS

LAN security, LANenforcer, IPS, Security hardware, Multi-core processor

For More Details: http://aircconline.com/ijcnc/V10N3/10318cnc02.pdf

Volume Link : http://airccse.org/journal/ijc2018.html

REFERENCES

[1]    Suricata Features, http://suricata-ids.org/features/all-features/

[2]   A performance analysis of snort and suricata network intrusion detection and prevention engines.IDCS 2011, the Fifth International Conference on Digital Society, Gosier, Guadeloupe, France. 187–192.

[3] Deployment of Intrusion Prevention System based on Software Defined Networking, 2013 15th IEEE International Conference on Communication Technology (ICCT)

[4]    Metaflows and its features, http://www.metaflows.com/features/ids/

[5]   Free and open source intrusion detection systems: A study, 2015 International Conference on Machine Learning and Cybernetics

[6]  Fundamentals of Iptables, http://www.thegeekstuff.com/2011/01/IPTABLES-FUNDAMENTALS/

[7]    Iptables, https://help.ubuntu.com/community/IptablesHowTo

[8]     About Nfqueue, http://netfilter.org/projects/libnetfilter_queue/

[9] Packet path through Kernel, http://www.cs.wustl.edu/~jain/cse567-11/ftp/pkt_recp/index.html

[10]Suricata.yamlhttps://redmine.openinfosecfoundation.org/projects/suricata/wiki/Suricatayaml

[11]   Tcpreplay, http://tcpreplay.synfin.net/wiki/tcpreplay

[12] Usage of nfqueue, https://home.regit.org/netfilter-en/using-nfqueue-and-libnetfilter_queue/

[13]   Setting  up  Suricata  in  inline  mode, https://redmine.openinfosecfoundation.org/projects/suricata/wiki/Setting_up_IPSinline_for_Lix

[14]   Ubuntu  Installation  steps  for  Suricata, https://redmine.openinfosecfoundation.org/projects/suricata/wiki/Ubuntu_Installation

[15]  Tuning  Suricata  Inline  IPS  performance –  discussion, https://lists.openinfosecfoundation.org/pipermail/oisf-users/2011-December/001141.html

[16]  Patrick-patch for zero copy, http://home.regit.org/2011/08/patrick-mchardy-memory mappednetlinkand-nfnetlink_queue/

[17]  Suricata  as  a  bridging  IPS (Setup),http://taosecurity.blogspot.in/2014/01/suricata-20beta2-    as-ipsonubuntu-1204.html

[18]  Emerging – Threats  Ruleset  Download, https://rules.emergingthreats.net/open/suricata/rules/

[19] Suricata Threading, https://kaurikim.wordpress.com/2015/02/16/suricata-threading/

[20]   Tomahawk,http://tomahawk.sourceforge.net/

An Effective Privacy-Preserving Data Coding in Peer-To-Peer Network

Ngoc Hong Tran1, Cao Vien Phung 2, Binh Quoc Nguyen 1, and Leila Bahri 3

Vietnamese-German University 1, Vietnam, Technische Universität Carolo-Wilhelmina zu Braunschweig2, Germany, and KTH 1, Sweden

ABSTRACT

Coding Opportunistically (COPE) is a simple but very effective data coding mechanism in the wireless network. However, COPE leaves risks for attackers easily getting the private information saved in the packets, when they move through the network to their destination nodes. Hence, a lightweight cryptographic approach, namely SCOPE, was proposed to consolidate COPE against the honest-but-curious and malicious attacks. Honest-but-curious attack serves adversaries who accurately obey the protocol but try to learn as much private information as possible for their curiosity. Additionally, this kind of attack is not destructive consequently. However, it may leave the backdoor for the more dangerous attacks carrying catastrophes to the system. Malicious attack tries to learn not only the private information but also modifies the packet on harmful purposes. To cope with this issue, in this work, a lightweight cryptographic approach improves COPE, namely SCOPE, that is defensive to the both attacks. The private information in the COPE packet are encrypted by Elliptic Curve Cryptography (ECC), and an additional information is inserted into SCOPE packets served for the authentication process using the lightweight hash Elliptic Curve Digital Signature Algorithm (ECDSA). We then prove our new protocol is still guaranteed to be a secure method of data coding, and to be light to effectively operate in the peer-to-peer wireless network

KEYWORDS

Network Coding, Peer-to-Peer, Homomorphic Encryption, Elliptic Curve Cryptography (ECC), Elliptic Curve Digital Signature Algorithm (ECDSA), Honest-But-Curious Attack, Malicious Attac.

For More Details :   http://aircconline.com/ijcnc/V10N3/10318cnc05.pdf

Volume Link : http://airccse.org/journal/ijc2018.html

REFERENCES

[1]    S. Katti, H. Rahul, W. Hu, D. Katabi, M. Mdard, and J. Crowcroft, “Xors in the air: Practical wireless network coding,” in Proc. ACM SIGCOMM, 2006, pp. 243–254. Cited on page(s): 1,6

[2]   V. Katiyar, K. Dutta, and S. Gupta, “A survey on elliptic curve cryptography for pervasive computing environment,” International Journal of Computer Applications, vol. 11, no. 10, pp. 41–46, 2010. Cited on page(s): 2

[3]  N. F. 186-4, “Elliptic curve digital signature algorithm (ecdsa),” NIST, Tech. Rep., 2013. Cited on page(s): 2, 7

[4]    N. Cai and R. Yeung, “Network coding and error correction,” in Proceedings of the 2002 IEEE      Information Theory Workshop, 2002, p. 119–122. Cited on page(s): 2

[5]  V. Talooki, R. Bassoli, D. Lucani, J. Rodriguez, F. Fitzek, H. Marques, and R. Tafazolli, “Security concerns and countermeasures in network coding based communication systems: A survey,” Computer Networks, vol. 83, pp. 422–445, 2015. Cited on page(s): 2

[6]  N. Cai and R. Yeung, “Secure network coding,” in IEEE International Symposium on Information Theory, 2002, p. 323. Cited on page(s): 2

[7]  Y. Fan, Y. Jiang, H. Zhu, J. Chen, and X. Shen, “Network coding based privacy preservation against traffic analysis in multi-hop wireless networks,” IEEE Transactions on Wireless Communications, vol. 10, no. 3, pp. 834–843, 2011. Cited on page(s): 2 [8] P. Paillier, “Public-key cryptosystems based on composite degree residuosity classes,” in Eurocrypt,  vol. 99, 1999, pp. 223–238. Cited on page(s): 2

[9]     A. Esfahani, G. Mantas, V. Monteiro, K. Ramantasy, E. Datsikay, and J. Rodriguez, “Analysis of a homomorphic mac-based scheme against tag pollution in rlnc-enabled wireless networks,” in IEEE 20th International Workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD), 2015, pp. 156–160. Cited on page(s): 2

[10]   X. Li, F. Fu, X. Zhao, and G. Wang, “Two improved homomorphic mac schemes in network coding,” in IEEE Fuzzy Systems and Knowledge Discovery (FSKD), 2015, pp. 2214–2219. Cited on page(s): 2

[11]   A. Esfahani, G. Mantas, J. Rodriguez, A. Nascimento, and J. Neves, “A null space-based mac scheme against pollution attacks to random linear network coding,” in IEEE Communication Workshop (ICCW), 2015, pp. 1521–1526. Cited on page(s): 2

[12]   C. Li, L. Chen, R. Lu, and H. Li, “Comment on ”an efficient homomorphic mac with small key size for authentication in network coding”,” IEEE Transactions on Computers, vol. 64, no. 3, pp. 882–883, 2015. Cited on page(s): 2

[13]   F. .-. with Change Notice 1, “Sha-2,” NIST, Tech. Rep., 2008. Cited on page(s): 13

Improvements in Routing Algorithms to Enhance Lifetime of Wireless Sensor Networks

Naga Ravikiran1 And C.G. Dethe2

1research Scholar, Ece Department, Priyadarshini Institute Of Engineering And Technology (Piet), Nagpur, Maharashtra.

2director, Ugc-Human Resource Development Centre, Rtm Nagpur University, Nagpur, India.

ABSTRACT

Wireless sensor network (WSN) brings a new paradigm of real-time embedded systems with limited computation, communication, memory, and energy resources that are being used fora huge range of applications. Clustering in WSNs is an effective way to minimize the energy consumption of sensor nodes. In this paper improvements in various parameters are compared for three different routing algorithms. First, it is started with Low Energy Adaptive Cluster Hierarchy (LEACH)which is a famed clustering mechanism that elects a CH based on the probability model. Then, work describes a Fuzzy logic system initiated CH selection algorithm for LEACH. Then Artificial Bee Colony (ABC)which is an optimisation protocol owes its inspiration to the exploration behaviour of honey bees. In this study ABC optimization algorithm is proposed for fuzzy rule selection. Then, the results of the three routing algorithms are compared with respect to various parameters.

KEYWORDS

Wireless Sensor Network (WSN), LEACH, Clustering, Artificial Bee Colony (ABC), Fuzzy logic system.

For More Details :   http://aircconline.com/ijcnc/V10N2/10218cnc03.pdf

Volume Link : http://airccse.org/journal/ijc2018.html

 REFERENCES

[1]   Abad, M.F.K. and Jamali, M.A.J. (2011) ‘Modify LEACH algorithm for wireless sensor network’, IJCSI International Journal of Computer Science Issues, Vol. 8, No. 5.

[2]  Abraham, A., Jatoth, R.K. and Rajasekhar, A. (2012) ‘Hybrid differential artificial bee colony algorithm’, Journal of Computational and Theoretical Nanoscience, Vol. 9, No. 2, pp.249–257.

[3]  Selvakumar, K., &Selvi, M. S. (2014). Efficient Load Balanced Routing Algorithm Based On Genetic And Particle Swarm Optimization.

[4]  Manjusha, M. S., &Kannammal, K. E. (2014). Efficient Cluster Head Selection Method For Wireless Sensor Network

[5]  Bee-Sensor-C: An Energy-Efficient and Scalable Multipath Routing Protocol for Wireless Sensor Networks.Celik, F., Zengin, A. and Tuncel, S. (2010)

[6]   ‘A survey on swarm intelligence based routing protocols in wireless sensor networks’, International Journal of Physical Sciences, Vol. 5, No. 14, pp.2118–2126.

[7]    Saini, M., &Saini, R. K. (2013). Solution of Energy-Efficiency of sensor nodes in Wireless sensor Networks. International Journal of Advanced Research in Computer Science and Software Engineering, 3(5), 353-357.

[8]   Han, L. (2010, October). LEACH-HPR: An energy efficient routing algorithm for Heterogeneous WSN. In Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference on (Vol. 2, pp. 507-511).IEEE.

[9]  Gou, H., &Yoo, Y. (2010, April). An energy balancing LEACH algorithm for wireless sensor networks. In Information Technology: New Generations (ITNG), 2010 Seventh International Conference on (pp. 822-827). IEEE.

[10] Farooq, M. O., Dogar, A. B., & Shah, G. A. (2010, July). MR-LEACH: multi-hop routing with low energy adaptive clustering hierarchy. In Sensor Technologies and Applications (SENSORCOMM), 2010 Fourth International Conference on(pp. 262-268). IEEE.

[11] El-Saadawy, M., &Shaaban, E. (2012, May). Enhancing S-LEACH security for wireless sensor networks.In Electro/Information Technology (EIT), 2012 IEEE International Conference on (pp. 1-6).IEEE.

[12] Chang, J-Y. andJu, P-H. (2012) ‘An efficient cluster-based power saving scheme for wireless sensor networks’, EURASIP Journal on Wireless Communications and Networking, Article 172, Vol. 2012.

[13] Hadjila, M., Guyennet, H. and Feham, M. (2013) ‘Energy- efficient in wireless sensor networks using fuzzy C-means clustering approach, International Journal of Sensors and Sensor Networks, Vol. 1, No. 2, pp.21–26.

[14] Hemavathi, N. and Sudha, S. (2014) ‘A fuzzy based predictive cluster head selection scheme for wireless sensor networks’, in The Proceedings of 8th International Conference on Sensing Technology & International Journal on Smart Sensing and Intelligent Systems, pp.560–567.

[15] Jerusha, S., Kulothungan, K. and Kannan, A. (2012) International Journal of Computer & Communication Technology, Vol. 3, No. 5, pp.0975–7449.

[16] Kaur, J. and Soni, N. (2015) ‘Performance evaluation of on demand energy efficient routing protocol for WSN’, International Journal of Future Generation Communication and Networking, Vol. 8, No. 5, pp.81–88.

[17]  Khalid, H., Abdullah, K.M., AhsanAwan, F. and Hussain, A. (2013) ‘Cluster head election schemes for WSN and MANET: a survey’, World Applied Sciences Journal, Vol. 23, No. 5, pp.611–620.

[18] Kour, H. and Sharma, A.K. (2010) ‘Hybrid energy efficient distributed protocol for heterogeneous wireless sensor network’, International Journal of Computer Applications, July, Vol. 4, No. 6,pp.0975–8887.

[19] Malarvizhi, M. and Gnanambal, I. (2015) ‘Harmonics elimination in multilevel inverter with unequal DC sources by fuzzy-ABC algorithm’, Journal of Experimental & Theoretical Artificial Intelligence, Vol. 27, No. 3, pp.273–292.

[20]   Nayak, P. and Devulapalli, A. (2016) ‘A fuzzy logic-based clustering algorithm for WSN to extend the network lifetime’, Sensors Journal, IEEE, Vol. 16, No. 1, pp.137–144.

[21] Ran, G., Zhang, H. and Gong, S. (2010) ‘Improving on LEACH protocol of wireless sensor networks using fuzzy logic’, Journal of Information & Computational Science, Vol. 7, No. 3, pp.767–775.

[22] Rana, S., Bahar, A. N., Islam, N., & Islam, J. (2015). Fuzzy Based Energy Efficient Multiple Cluster Head Selection Routing Protocol for Wireless Sensor Networks.

[23]  Kumar, R., &Prakash, N., (2013) Energy Efficient Approach for Wireless Sensor Network, 3(6)

[24]   Singh, S. P., & Sharma, S. C. (2015). A Survey on Cluster Based Routing Protocols in Wireless Sensor Networks. Procedia Computer Science, 45, 687-695.

[25] Taruna, S., &Shringi, S. (2013). A cluster based routing protocol for prolonging network lifetime in heterogeneous wireless sensor networks. Taruna et al., International Journal of Advanced Research in HYBRIDComputer Science and Software Engineering, 3(4), 658-665.

[26]  Yoon, M., & Chang, J. (2011, September). Design and implementation of cluster-based routing protocol using message success rate in sensor networks. In HPCC, 2011 IEEE 13th International Conference on (pp. 622-627).IEEE.

[27]  PhanThiThe, Ngo QuangQuyen, Vu Ngoc Phan and Tran Cong Hung. (2017). A Proposal to Improve SEP Routing Protocol Using Insensitive Fuzzy C-Means in Wireless Sensor Network, International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017.

[28] SaeidPourroostaeiArdakani. (2017). Data aggregation routing protocols in wireless sensor networks: a taxonomy, International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.2, March 2017.

[29] Tran Cong Hung and Ly Quoc Hung. (2016).Energy consumption improvement of traditional clustering method in wireless sensor network, International Journal of Computer Networks & Communications (IJCNC) Vol.8, No.5, September 2016.

Lightweight Cryptography For Distributed PKI Based MANETS

N Chaitanya Kumar, Abdul Basit, Priyadarshi Singh, and V. Ch. Venkaiah

School of Computer and Information Sciences, University of Hyderabad,Hyderabad-500046, India

ABSTRACT

Because of lack of infrastructure and Central Authority(CA), secure communication is a challenging job in MANETs. A lightweight security solution is needed in MANET to balance its nodes resource tightness and mobility feature. The role of CA should be decentralized in MANET because the network is managed by the nodes themselves without any fixed infrastructure and centralized authority. In this paper, we created a distributed PUblic Key Infrastructure (PKI) using Shamir secret sharing mechanism which allows the nodes of the MANET to have a share of its private key. The traditional PKI protocols require centralized authority and heavy computing power to manage public and private keys, thus making them not suitable for MANETs. To establish a secure communication for the MANET nodes, we proposed a lightweight crypto protocol which requires limited resources, making it suitable for MANETs.

KEYWORDS

Secret sharing, Lightweight Cryptography, Public key cryptography, MANETS

 For More Details :   http://aircconline.com/ijcnc/V10N2/10218cnc07.pdf

 Volume Link : http://airccse.org/journal/ijc2018.html

REFERENCES

[1]  F. Anjum and P. Mouchtaris, ”Security for wireless ad hoc networks”, in: Wiley-Blackwell, Mar. 2007.

[2] Chai-Keong Toh (2002). ”Ad Hoc Mobile Wireless Networks: Protocols and Systems 1st Edition”, in: Prentice Hall PTR. Retrieved 2016-04-20.

[3] Vanesa Daza, Javier Herranz, Paz Morillo, Carla Rafols, ”Cryptographic techniques for mobile ad-hoc networks,” in: Computer Networks, Volume 51, Issue 18, 19 December 2007, Pages 4938-4950.

[4]  Y.-C. Hu, A. Perrig, and D. B. Johnson. Ariadne, ”A secure on-demand routing protocol for ad hoc networks”, in: Proceedings of the Eighth ACM International Conference on Mobile Computing and Networking(Mobicom 2002), September 2002.

[5] Y.C. Hu, A. Perrig, and D. B. Johnson, ”Packet leashes: A defense against wormhole attacks in wireless networks”, in: Proceedings of IEEE Infocom 2003,pp. 1976-1986 vol.3.

[6] X. Yao, X. Han and X. Du, ”A light-weight certificate-less public key cryptography scheme based on ECC,” 2014 23rd International Conference on Computer Communication and Networks (ICCCN), Shanghai, 2014, pp. 1-8.

[7] Rajashekarappa, K M Sunjiv Soyjaudah, Sumithra Devi K A, ”Study on Cryptanalysis of the Tiny Encryption Algorithm” in: International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 2, issue 3 (2013) pp. 88-91.

[8]   Y. Amir, Y.Kim, C. Nita-Rotaru, ”Secure communication using contributory key agreement”, in: IEEE Transactions on Parallel and Distributed systems, pp. 468-480, 2009.

[9]   N. Koblitz, ”Elliptic curve cryptosystems,” in: Mathematics of Computation, vol. 48, no.177, pp.203-209, Jan 1987.

[10]   G.R. Blakley, Safeguarding cryptographic keys, in: Proceedings of the National Computer Conference, American Federation of Information, Processing Societies Proceedings, vol. 48, 1979, pp. 313-317.

[11]   Singh, Nidhi, Appala Naidu Tentu, Abdul Basit, and V. Ch Venkaiah. ”Sequential secret sharing scheme based on Chinese remainder theorem.” In Computational Intelligence and Computing Research (ICCIC), 2016 IEEE International Conference on, pp. 1-6. IEEE, 2016.

[12]   A. Shamir, ”How to share a secret”, in: Communications of the ACM 22 (1979) 612-613.

[13]   Lidong Zhou and Z. J. Haas, ”Securing ad hoc networks,” in IEEE Network, vol. 13, no. 6, pp. 24-30, Nov/Dec 1999.

[14]   Koblitz, Neal. ”Elliptic curve cryptosystems.” in Mathematics of computation 48.177 (1987): 203-209.

[15]   Miller, Victor S. ”Use of elliptic curves in cryptography.” in: Conference on the Theory and Application of Cryptographic Techniques, Springer Berlin Heidelberg, 1985.

[16]   Tentu, Appala Naidu, Abdul Basit, K. Bhavani, and V. Ch Venkaiah. ”Multi-secret Sharing Scheme for Level-Ordered Access Structures.” In International Conference on NumberTheoretic Methods in Cryptology, pp. 267-278. Springer, Cham, 2017.

[17]   Feldman, Paul. ”A practical scheme for non-interactive verifiable secret sharing.” in: Foundations of Computer Science, 1987, 28th Annual Symposium on. IEEE, 1987.

[18]   Kumar, N. Chaitanya, Abdul Basit, Priyadarshi Singh, V. Ch Venkaiah, and Y. V. Rao. ”Node Authentication Using BLS Signature in Distributed PKI Based MANETS.” arXiv preprint arXiv:1708.08972 (2017).

[19]  N. Chaitanya Kumar, Abdul Basit, Priyadarshi Singh, V. Ch. Venkaiah, ”Proactive Secret Sharing for Long Lived MANETs Using Elliptic Curve Cryptography”, in: 2017 IEEE International Conference on Inventive Computing And Informatics (ICICI 2017), Coimbatore, India, 2017.(In press)

[20]   S. A. Yee Hunn, S. Z. binti Md. Naziri and N. binti Idris, ”The development of tiny encryption algorithm (TEA) crypto-core for mobile systems,” in: 2012 IEEE International Conference on Electronics Design, Systems and Applications (ICEDSA), Kuala Lumpur, 2012, pp. 45-49.

[21]   Basit, Abdul, N. Chaitanya Kumar, V. Ch Venkaiah, Salman Abdul Moiz, Appala Naidu Tentu, and Wilson Naik. ”Multi-stage Multi-secret sharing scheme for hierarchical access structure. In Computing, Communication and Automation (ICCCA), 2017 International Conference on, pp. 557-563. IEEE, 2017.

AUTHORS

N Chaitanya Kumar received M.Tech from JNTU Hyderabad, and he did Bachelor degree in computer science. Currently, he is pursuing his PhD in Computer Science from the University of Hyderabad. His research interests include Information security, Cryptography in MANET. Abdul Basit received Master of computer application from Jamia Hamdard UniGangtok. Currently, he is pursuing his PhD in Computer Science from the University of Hyderabad. His research interests include Information security, Cryptography, and Cyber security.

Priyadarshi Singh received M.Tech from IIT(ISM) Dhanbad. He did Bachelor degree in Information Technology. Currently, he is pursuing his PhD in Computer Science from the University of Hyderabad. His research interests include Cryptography, Public key infrastructure.

Ch. Venkaiah obtained his PhD in 1988 from the Indian Institute of Science (IISc), Bangalore in the area of scientific computing. He worked for several organisations including the Central Research Laboratory of Bharat Electronics, Tata Elxsi India Pvt. Ltd., Motorola India Electronics Limited, all in Bangalore. He then moved onto academics and served IIT, Delhi, IIIT, Hyderabad, and C R Rao Advanced Institute of Mathematics, Statistics, and Computer Science. He is currently serving the Hyderabad Central University. He is a vivid researcher. He designed algorithms for linear programming, subspace rotation and direction of arrival estimation, graph coloring, matrix symmetriser, integer factorisation, cryptography, knapsack problem, etc.

Trust Factor and Fuzzy-Firefly Integrated Particle Swarm Optimization Based Intrusion Detection And Prevention System For Secure Routing Of Manet

Ramireddy Kondaiah1and Bachala Sathyanarayana2

1Research Scholar, Department of Computer Science, Rayalaseema University, Kurnool

A.P,India.& Associate Professor, Dept of CSE, PBRVITS, Kavali, Andhra Pradesh India.

2 Professor in Computer Science &Technology ,Sri Krishnadevaraya University,Anantapur, A.P, India.

ABSTRACT

Mobile Ad hoc Networks (MANET) is one of the rapidly emanating technologies, which has gained attention in a wide range of applications in the fields of military, private sectors, commercials and natural calamities. Securing MANET is a dominant responsibility, and hence, a trust factor and fuzzy based intrusion detection and prevention system is proposed for routing in this paper. Based on the trust values of the nodes, the fuzzy system identifies the intruder, such that the path generated in the MANET is secured. Moreover, an optimization algorithm, entitled Fuzzy integrated Particle Swarm Optimization (FuzzyFPSO), is proposed by the concatenation of the Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) for the optimal path selection in order to provide secure routing. The simulation of the proposed methodology is NS2 simulator and analysis is carried out considering four cases, like without attack, flooding attacks, black hole attack and selective packet drop attack concerning throughput, delay and detection rate. The remarkable evaluation measures of the proposed Fuzzy-FPSO are the maximal throughput of 0.634, minimal delay of 0.044 , maximal detection rate of 0.697 and minimal routing overhead of 0.24550 And the evaluation measure for the case without any attacks are the maximal throughput of 0.762, minimal delay of 0.029 ,maximal detection rate of 0.805 and minimal routing overhead of 0.11511.

KEYWORDS

MANET, Routing, Trust, Fuzzy system, Firefly Algorithm, Particle Swarm Optimization.

For More Details :   http://aircconline.com/ijcnc/V10N1/10118cnc02.pdf

Volume Link : http://airccse.org/journal/ijc2018.html

REFERENCES

[1]   M. Nekovee, and R. S. Saksena,“Simulations of large-scale Wi-Fi-based wireless networks,” Interdisciplinary challenges and applications, Future Generation Computer Systems, vol. 26, no. 3, pp. 514–520, 2010.

[2]   Poonam Joshi, Pooja Nande, Ashwini Pawar, Pooja Shinde, and Rupali Umbare, “EAACK-A Secure Intrusion Detection And Prevention System For MANETS,” in proceedings of IEEE International Conference on Pervasive Computing (ICPC), pp. 1-6, 2015.

[3]   Babu, M. Rajesh, and G. Usha, “A Novel Honeypot Based Detection and Isolation Approach (NHBADI) to Detect and Isolate Black Hole Attacks in MANET,” Wireless Personal Communications, vol. 90, no. 2, pp. 831-845, 2016.

[4]   arrukh Aslam Khan, Muhammad Imran, Haider Abbas, and Muhammad Hanif Durad, “A detection and prevention system against collaborative attacks in Mobile Ad hoc Networks,” Future Generation Computer Systems, vol. 68, pp. 416-427, 2017

[5]    Basant Subba , Santosh Biswas, and Sushanta Karmakar, “Intrusion detection in Mobile Ad-hoc Networks: Bayesian game formulation,” Engineering Science and Technology, an International Journal, vol. 19, no.2, pp. 782-799, 2016.

[6]  Marchang, Ningrinla, Raja Datta, and Sajal K. Das, “A Novel Approach for Efficient Usage of Intrusion Detection System in Mobile Ad Hoc Networks,” IEEE Transactions on Vehicular Technology, vol. 66, no. 2, pp. 1684-1695, 2017.

[7]   Usha, G., M. Rajesh Babu, and S. Saravana Kumar, “Dynamic anomaly detection using cross layer security in MANET,” Computers & Electrical Engineering, pp.1-11, 2016.

[8]  Erfan A Shams, and Ahmet Rizaner, “A Novel Support Vector Machine Based Intrusion Detection System For Mobile Ad Hoc Networks,” Wireless Networks, pp. 1-9, 2017.

[9] Nadeem, Adnan, and Michael P. Howarth, “A survey of MANET intrusion detection & prevention approaches for network layer attacks,” IEEE communications surveys & tutorials, vol. 15, no. 4, pp. 2027-2045, 2013.

[10] Opinder Singh, Jatinder Singh, and Ravinder Singh, “An Intelligent Intrusion Detection and Prevention System for Safeguard Mobile Adhoc Networks against Malicious Nodes,” Indian Journal of Science and Technology, vol. 8, no. 1, pp. 1-12, 2017.

[11] Hamed Janzadeh, Kaveh Fayazbakhsh, Mehdi Dehghan, and Mehran S. Fallah, ”A secure creditbased cooperation stimulating mechanism for MANETs using hash chains,” Future Generation Computer Systems, vol. 25, no. 8, pp. 926-934, 2009.

[12] Soni M, Ahirwa M, and Aggarwal S, “A Survey on Intrusion Detection in MANET,” in proceedings of International Conference on Computational Intelligence and Communication Networks, pp. 1027- 1032, 2015.

[13] Djahel S, Farid N, and Zhang Z, “Mitigating Packet Dropping Problem in Mobile Ad Hoc Networks: Proposals and Challenges,” IEEE communications surveys and tutorials, vol. 13, no. 4, pp. 658-672,2011

[14]   H. Debar, M. Dacier and A.Wespi, “A Revised Taxonomy for Intrusion Detection Systems,” Annals of Telecommunications, Vol.55, No.7, pp 361-378, July 2000.

[15] Adnan Nadeem, and Michael Howarth, “A generalized intrusion detection and prevention mechanism for securing MANETs,” in proceedings of ICUMT09 International Conference on Ultra Modern Telecommunications and Workshops, pp. 1-6, 2009.

[16] Y. Harold Robinson and M. Rajaram, “Energy-Aware Multipath Routing Scheme Based on Particle Swarm Optimization in Mobile Ad Hoc Networks“, Scientific World Journal, Hindawi Publishing Corporation, Vol. 2015, Article ID 284276, pp. 1-9, 2015.

[17] Vikas Gupta , Ashok Verma, Ajay Lala and Ashish Chaurasia, “Scenario Based Performance and Comparative Simulation Analysis of Routing Protocols in MANET,” IJCSNS International Journal of Computer Science and Network Security, Vol.13, No.6, June 2013.

[18] Anupam Das and M. Mahfuzul Islam,”SecuredTrust: A Dynamic Trust Computation Model for Secured Communication in Multi-Agent Systems“, IEEE Transactions on dependable and secure computing, Vol.9, No.2, 2012

[19] A. Sharma and P.K. Johari, “Eliminating Collaborative Black-hole Attack by Using Fuzzy Logic in Mobile Ad-hoc Network,” International Journal of Computer Sciences and Engineering, Vol.5, No.5, pp. 2347-2693, May 2017.

[20] Mohammed Abdel-Azim, Hossam El-Din Salah and Menas Ibrahim, “Black Hole attack Detection using Fuzzy based IDS,” International Journal of Communication Networks and Information Security (IJCNIS), Vol.9, No.2, August 2017.

[21] Iztok Fister, IztokFisterJr, Xin-SheYang and JanezBrest, “A comprehensive review of firefly algorithms,” Swarm and Evolutionary Computation, Vol.13, pp.34-46, December 2013.

[22] Xin-She Yang, “Firefly Algorithm, Nature-Inspired Metaheuristic Algorithms,” Computers, Luniver Press, pp.79–90, 2008.

[23] Dongshu Wang, Dapei Tan and Lei Liu, “Particle Swarm Optimization Algorithm: An overview,” Soft Computing, pp.1–22, January 2017.

[24] Dian Palupi Rini, Siti Mariyam Shamsuddin and Siti Sophiyati Yuhaniz, “Particle Swarm Optimization: Technique, System and Challenges,” International Journal of Computer Applications, Vol.14, No.1, January 2011.

[25] Mahmood K. Ibrahem , Ameer M. Aboud, “A Secure Routing Protocol for MANET,” International Journal of Computer Science Engineering and Technology( IJCSET), Vol.4, No.7, July 2014.

[26]  Nisha Soms and P.Malathi, “Evolution of Intrusion Detection System in MANETs – A Survey,” International Journal of Innovations & Advancement in Computer Science(IJIACS), Vol.6, No.5, May 2017.

AUTHORS

author-4

Mr.Ramireddy Kondaiah received his B.Sc Degree in Mathematics, Physics and Chemistry from Sri Venkateswara University,Tirupti, A.P , India in 1996, Master of Computer Applications from Sri Krishna Devaraya University Campus College affiliated to Sri Krishna Devaraya University in 2000.Now He is pursuing Ph.D. from Rayalaseema University,Kurnool ,AndhraPradesh,India. His research areas Include Computer Networks/MANET Routing with Intrusion Detection.

author-5

Prof. B. Sathyanarayana received his B.Sc Degree in Mathematics, Economics and Statistics from Madras University, India in 1985, Master of Computer Applications from Madurai Kamaraj University in 1988. He did his Ph.D in Computer Networks from Sri Krishnadevaraya University, Anantapur, A.P. India. He has 24 years of teaching experience. His Current Research Interest includes Computer Networks, Network Security and Intrusion Detection. He has published 30 research papers in National and International journals.

 

 

 

 

                      

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