Wormhole Attack Detection and Prevention Model in MANET Based on Hop-Count and Localization

Abstract

Due to the nature of wireless transmission in Mobile Ad-Hoc Networks (MANET), has more security issues compared to wired networks. Among of all of these security issues, wormhole attack is considered to be a very serious security thread over MANET and it's classified as a network layer attack. In this attack, two selfish nodes which is geographically very far away to each other, form a tunnel between each other to hide their actual location and try to believe that they are true neighbors and therefore make conversation through the wormhole tunnel. Consequently, the two selfish nodes will completely disrupt the communication channel. In this thesis, we address the problem of identifying and isolating nodes which form wormhole attack. A new model is developed for detection and prevention of wormholes based on range-free localization scheme. The proposed model effectively and efficiently isolates both wormhole node and colluding node. The proposed model integrates the trust factor model, the route establishment, and the detection and prevention of misbehaving nodes. More precisely, the proposed model consists of four modules: the localization module, the trust factor module, the route establishment module, and the detection and prevention module. All four modules are tightly integrated to ensure that multi-hop communications take place over paths free from malicious nodes. Our model allows the evaluation of node behavior on a pre-packet basis and without the need for more energy consumption or computation-expensive techniques. We show via simulation that proposed model successfully avoids misbehaving nodes which makes proposed model an attractive choice for MANET environments. The comparison of proposed model against Secure-AODV has been presented in terms of average hop-count, detection rate and accuracy of detection. It is found that the proposed model achieves an acceptable detection rate about 99.7% versus 99.2% for Secure-AODV model and a detection accuracy rate 98.4% versus 97.1 for Secure-AODV.