ID-WNFS: Intrusion Detection Using Whale Neuro-Fuzzy System In Wireless Sensor Network
DOI:
https://doi.org/10.26438/ijcse/v6i11.6071Keywords:
Intrusion detection, wireless sensor network (WSN), routing, Neuro-Fuzzy System, whale optimization algorithmAbstract
Intrusion detection in wireless sensor network (WSN) is a challenging research area, as the WSN has vast area, and lot of nodes. The wireless communication among the nodes, and the battery life of the nodes, makes the researchers difficult to establish a proper communication through the routing mechanism. This research develops the intrusion detection model by using the Neuro fuzzy model. The proposed Intrusion detection using Whale Neuro-Fuzzy System (WNFS) (ID-WNFS) is developed here for detecting the intruders present in the WSN environment. The proposed ID-WNFS has two components, sniffer for creating the log file, and detector for anomaly detection. The sniffer creates the log file by examining the transmission information and extracts the necessary features. The extracted features are sent to the detector, which has the WNFS for the anomaly detection. The proposed WNFS is created by including the properties of the whale optimization algorithm (WOA) with the Neuro fuzzy architecture. The optimization algorithm selects the appropriate fuzzy rules for the detection. The proposed ID-WNFS notifies the simulation protocol about the anomaly behaviour, and thus the routing path is built for the WSN. The entire simulation of ID-WNFS is done by introducing various attacks on nodes and the result reveal that, the ID-WNFS has achieved with the network lifetime as 43.989, energy as 7.106808 and the detection accuracy as 0.787191
References
[1] N. Assad, B.Elbhiri, M. A. Faqihi, M. Ouadou , D. Aboutajdine, "Efficient deployment quality analysis for intrusion detection in wireless sensor networks", Wireless Networks, vol. 22, Issue. 3, pp991-1006, 2016.
[2] H. Moosavi,F. M. Bui, "A Game-Theoretic Framework for Robust Optimal Intrusion Detection in Wireless Sensor Networks", IEEE Transactions on Information Forensics and Security, vol. 9, Issue. 9, pp. 1367-1379, 2014.
[3] A. K. Sagar,D. K. Lobiyal, "Probabilistic Intrusion Detection in Randomly Deployed Wireless Sensor Networks", Wireless Personal Communications, vol. 84, no. 2, pp. 1017-1037, 2015.
[4] L. Gheorghe, R. Rughinis , R. Tataroiu, "Adaptive Trust Management Protocol based on Intrusion Detection for Wireless Sensor Networks", In proceedings of IEEE International Conference on Networking in Education and Research, pp. 1-7, 2013.
[5] M. Wazid, A. K. Das, "An Efficient Hybrid Anomaly Detection Scheme Using K-Means Clustering for Wireless Sensor Networks", Wireless Personal Communications, vol. 90, Issue 4, pp. 19712000, 2016.
[6] A. Saeed, A. Ahmadinia, A. Javed, H. Larijani, "Random Neural Network based Intelligent Intrusion Detection for Wireless Sensor Networks", In proceedings of International Conference on Computational Science, vol. 80, pp. 2372-2376, 2016.
[7] S.Shamshirband, A. Amini, N. B. Anuar, L. M. Kiah, T. Y. Wah , S. Furnell, "D-FICCA: A Density-based Fuzzy Imperialist Competitive Clustering Algorithm for Intrusion Detection in Wireless Sensor Networks", Measurement, vol. 55, pp. 212-226, 2014.
[8] P. Rutravigneshwaran, “A Study of Intrusion Detection System using Efficient Data Mining Techniques’, International Journal of Scientific Research In Network Security and communication , vol. 5,no. 6,pp 5-8,2017.
[9] I.Butun, Salvatore D. Morgera, R. Sankar, "A Survey of Intrusion Detection Systems in Wireless Sensor Networks", In proceedings of IEEE International Conference on Modeling, Simulation and Applied Optimization, pp. 1-6, 2015.
[10] Y. Wang, X. Wang, B. Xie, D. Wang , Dharma P. Agrawal, "Intrusion Detection in Homogeneous and Heterogeneous Wireless Sensor Networks", IEEE Transactions on Mobile computing, vol. 7, no. 6, pp. 698-710, 2008.
[11] A. Abduvaliyev, Al-Sakib Khan Pathan, J. Zhou, R. Roman, WaiChoong Wong, "On the Vital Areas of Intrusion Detection Systems in Wireless Sensor Networks", IEEE Communications Surveys & Tutorials, vol. 15, no. 3, pp. 1223-1237, 2013.
[12] Sandhya G , A. Julian, "Intrusion Detection in Wireless Sensor Network Using Genetic K-Means Algorithm", In proceedings of IEEE International Conference on Advanced Communication Control and Computing Teclmologies, pp. 1-4, 2014.
[13] G. Han, J. Jiang, W. Shen, L. Shu, J. Rodrigues, "IDSEP: a novel intrusion detection scheme based on energy prediction in clusterbased wireless sensor networks", IET Information Security, vol. 7, no. 2, pp. 97-105, 2013.
[14] H. M. Salmon, C. M. d. Farias, P. Loureiro, L. Pirmez, "Intrusion Detection System for Wireless Sensor Networks Using Danger Theory Immune-Inspired Techniques", International Journal of Wireless Information Networks, vol. 20, Issue 1, pp. 39-66, 2013.
[15] S. Shamshirband, N.B. Anuar, M.L.M. Kiah, A. Patel, "An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique", Engineering Applications of Artificial Intelligence, vol. 26, no. 9, pp. 2105-2127, 2013.
[16] S. Rajasegarar, C. Leckie, M. Palaniswami, "Hyperspherical cluster based distributed anomaly detection in wireless sensor networks", Journal of Parallel and Distributed Computing, vol. 74, no. 1, pp. 1833-1847, 2014.
[17] M. Elleuch, O. Hasan, S. Tahar, M. Abid, "Formal probabilistic analysis of detection properties in wireless sensor networks", Formal Aspects of Computing, vol. 27, no. 1, pp. 79-102, 2015.
[18] S. Mirjalili, A. Lewis, "The Whale Optimization Algorithm", Advances in Engineering Software Vol.95, pp. 51–67, 2016.
[19] S. Shamshirband, N. B. Anuar, L. M. Kiah, "Co-FAIS:
Cooperative fuzzy artificial immune system for detecting intrusion in wireless sensor networks", Journal of Network and Computer Applications, vol. 42, pp.102-117, 2014.
[20] M. Riecker, S. Biedermann, R. E. Bansarkhani, M. Hollick, "Lightweight energy consumption-based intrusion detection system for wireless sensor networks", International Journal of Information Security, vol. 14, Issue 2, pp.155-167, 2015.
[21] Guorui Li, Jingsha He,Yingfang Fu, "Group-based intrusion detection system in wireless sensor networks", Computer Communications, vol. 31, no. 18, pp. 4324-4332, 2008.
[22] M. Moshtaghi, C. Leckie, S. Karunaseker , S. Rajasegarar, "An adaptive elliptical anomaly detection model for wireless sensor networks", Computer Networks, vol. 64, pp. 195-207, 2014.
[23] A. Yadav, V.K. Harit, “Fault Identification in Sub-Station by
Using Neuro-Fuzzy Technique”, International Journal of Scientific Research in Computer Science and Engineering,Vol. 4. No. 6,pp 1-7,2016.
[24] W. R. Heinzelman, A. Chandrakasan, H. Balakrishnan, “EnergyEfficient Communication Protocol for Wireless Microsensor Networks,” In Proceedings of the 33rd Hawaii International Conference on System Sciences – 2000,IEEE,pp. 1-10,2000.
[25] KDD Cup 1999. Available on: http://kdd.ics. uci.edu/databases /kddcup99/kddcup99.html, October 2007
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