A Hybridized Immune System for Avoidance of Wormhole Attacks in Manet

Abstract

In Mobile Ad-hoc Networks (MANETs), the most challenging task is detecting and mitigating wormhole links during data transmission between the source and destination nodes. Since it requires special hardware, synchronized clocks, mobile nodes equipped with GPS, etc. To overcome these challenges, Artificial Immune System-based Improved Secureaware Wormhole Attack Detection (AIS-ISWAD) technique has been proposed that considers maximum end-to-end delay, path length and system parameters for detecting wormhole attacks. To simplify the detection process, AIS has been used as a learning approach that learns those parameters to detect the wormhole links and select an alternative route for transmitting the data packets. However, an uncertainty problem is addressed in AIS during computation of affinity value between antibody and antigen. Also, route selection is still not satisfied since data transmission requires a high-performance stable path from source to destination nodes. Therefore, the main goal of this article is handling the uncertainty problem during affinity computation and selecting the high-performance stable paths to transmit the data. In this paper, a Fuzzy Logic and AIS-ISWAD (FLAIS-ISWAD) technique is proposed to improve the wormhole attack detection and mitigation. In this technique, all computed parameters are given to the FL system to handle the uncertainty problem and construct the high-performance stable paths among all available paths in the network. Also, AIS is applied as a learning method to identify and isolate the wormhole links/nodes in MANETs with the highest network performance. Finally, the performance of the FLAIS-ISWAD technique is evaluated and compared through simulation results in terms of different performance metrics.