Intrusion detection has paramount importance in network security. Intrusion detection depends on energy dissipation, whereas trust remains a hectic factor. In this paper, a trust-aware scheme is proposed to detect intrusion in Mobile Ad Hoc Networking (MANET). The proposed method uses Piecewise Fuzzy C-Means Clustering (pifCM) and fuzzy Naive Bayes (fuzzy NB) for the intrusion detection in the network. The pifCM helps to determine the cluster heads from the clusters. After the selection of cluster heads, the intrusion in the network is determined using fuzzy Naive Bayes with the help of node trust table. The node trust table contains the updated trust factors of all the nodes and the presence of intruded nodes are found with the help of the trust table. After the intrusion is detected, they are eliminated and this reduces the delay in transmission. The effectiveness of the proposed method is analyzed based on the metrics, such as throughput, detection rate, delay, and energy. The proposed method has the delay at the rate of 0.003, energy dissipation of 0.657, the detection rate of 9.85, and throughput of 0.659.
Singh O, Singh J, and Singh R, " Multi-level trust based intelligence intrusion detection system to detect the malicious nodes using elliptic curve cryptography in MANET,"Cluster Computing, 21(1), 51–63, 2018.
Subba B, Biswas S, and Karmakar S,” Intrusion detection in Mobile Ad hoc Networks: Bayesian game formulation,”Engineering Science and Technology, an International Journal, 19(2), 782–799, 2016.
Neenavath Veeraiah, B. Tirumala Krishna, "Trust-aware Fuzzy Clus-Fuzzy NB: intrusion detection scheme based on fuzzy clustering and Bayesian rule," Wireless Networks, pp 1–15, 2019.
Sumaiya Thaseen Ikram, Aswani Kumar Cherukuri,"Intrusion detection model using fusion of chi-square feature selection and multi class SVM," Journal of King Saud University - Computer and Information Sciences, 29, 462– 472, 2017.
Storr H. H. P, Xu Y, and Choi J, ” A compact fuzzy extension of the Naive Bayesian classification algorithm,” In Proceedings of InTech/VJFuzzy, 2002.
Marchang N, Datta R, and Das S. K," A novel approach for efficient usage of intrusion detection system in mobile ad hocnetworks,"IEEE Transactions on Vehicular Technology, 66(2), 1684–1695, 2017.
Yian Huang and Wenke Lee ,"A Cooperative Intrusion Detection System for Ad Hoc Networks," Proceedings of the 1st ACM workshop on Security of ad hoc and sensor networks, Pages 135-147, 2003.
Dorothy E. Denning ,"An Intrusion-Detection Model," IEEE transactions on software engineering, vol. se-13, no. 2, february 1987.
Mostafa A. Salama, Heba F. Eid, Rabie A. Ramadan, Ashraf Darwish, and Aboul Ella Hassanien,"Hybrid Intelligent Intrusion Detection Scheme,"Soft Computing in Industrial Applications, pp 293-303, 2011.
E. Biermann, E. Cloete and L.M. Venter, ”A comparison of intrusion detection Systems”, Computer and Security, vol. 20, pp. 676-683, 2001.
T. Verwoerd and R. Hunt, ”Intrusion detection techniques and approaches”, Computer Communications, vol. 25, pp.1356-1365, 2002.
S. D. Thepade and P. Bidwai, "Iris recognition using fractional coefficients of transforms, Wavelet Transforms and Hybrid Wavelet Transforms," 2013 International Conference on Control, Computing, Communication and Materials (ICCCCM), Allahabad, 2013, pp. 1-5.
S. Otoum, B. Kantarci and H. T. Mouftah, "On the Feasibility of Deep Learning in Sensor Network Intrusion Detection," in IEEE Networking Letters, vol. 1, no. 2, pp. 68-71, June 2019.
J. Zuniga-Mejia, R. Villalpando-Hernandez, C. Vargas-Rosales and A. Spanias, "A Linear Systems Perspective on Intrusion Detection for Routing in Reconfigurable Wireless Networks," in IEEE Access, vol. 7, pp. 60486- 60500, 2019.