Hybrid machine learning approach for trust evaluation to secure MANET from routing attacks

Article type: Research Article

Authors: Meenakshi, K.^{a; b; *} | Revathi, M.^c | Harsha, Sanda Sri^c | Tamilarasi, K.^d | Shanthi, T.S.^e | Sugumar, D.^f | Suriyakrishnaan, K.^g | Uma Maheswari, B.^h | Rajaram, A.ⁱ

Affiliations: [a] School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, India | [b] M.C.A Department, MEASI Institute of Information Technology, Rayapettah | [c] Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation Vaddeswaram, Andhra Pradesh | [d] School of Computer Science and Engineering, Vellore Institute of Technology University, Chennai | [e] Department of Computer Science, S.A.V Sahaya Thai Arts and Science (Women) College, Vadakkankulam, Tirunelveli | [f] Department of Electronics and Communication Engineering, Sona College of Technology, Salem | [g] Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences (Deemed to be University), Coimbatore, India | [h] Department of Computer Science and Engineering, St. Joseph’s College of Engineering, OMR, Chennai | [i] Department of Electronics and Communication Engineering, E.G.S Pillay Engineering College, Nagapattinam, India

Correspondence: [*] Corresponding author. K. Meenakshi, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India. E-mail: [email protected].

Abstract: A new era in communication has been ushered in by MANET networks, in which users (nodes) interact with one another through a self-configuring network of handheld devices linked by wireless links. Nodes are capable of participating and enthusiastic about sending packets to other nodes. Consequently, the need for a routing protocol materializes. The most difficult aspect is dealing with the network’s dynamic topology as a result of node mobility. This is because limited resources like storage space, battery life, and bandwidth require a protocol that can quickly adapt to topology changes while periodically updating messages. On the other hand, security is another important aspect of routing since the involvement of attackers will exhaust the network resources. This paper addresses the main issue of designing a routing protocol that handles all the adversaries and achieves better efficiency. For that, we proposed a Hybrid Machine Learning (HyML) model which evaluates the. Initially, the network is segregated by the Secure Stable Clustering (SSC) approach which first verifies the node’s legacy and forms clusters based on stability. The HyML is designed by combining two important ML techniques such as ANN and fuzzy-C Means (FCM) algorithm. The ANN model learns multiple attributes of the trust value and computes the cumulative trust score. Next, FCM determines the node position upon trust score. After the computation of the trust value, optimal route selection is performed by the Spider Monkey Optimization (SMO) technique. The overall work is evaluated through comprehensive simulations based on network longevity, throughput, energy usage, PDR, attack detection efficiency, and delay.

Keywords: Hybrid machine learning, ANN, routing attacks, MANET

DOI: 10.3233/JIFS-231918

Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3429-3445, 2024