Support Vector Machine Based Determining Attackers and Localizing Adversaries in Wireless Networks

Abstract: Wireless spoofing attacks are easy to launch and can significantly impact the performance of networks. Although the identity of a node can be verified through cryptographic authentication, conventional security approaches are not always desirable because of their overhead requirements. The traditional approaches uses the spatial correlation of received signal strength (RSS) inherited from wireless nodes to detect the spoofing attacks and then formulate the problem of determining the number of attackers as a multiclass detection problem. Cluster-based mechanisms are developed to determine the number of attackers. In addition, they developed an integrated detection and localization system that can localize the positions of multiple attackers. The existing techniques are used to detect attackers but don’t know how it attacks. In this paper, extend the RSS techniques to find out how attackers will attack by monitoring the attacker’s activities.

Keywords: Wireless network security, spoofing attack, attack detection, localization.