Paper Title
SVM Based Determining Attackers And Localizing Adversaries In Wireless Net Works

Abstract- Wireless spoofing attacks are easy to launch and can significantly impact the performance of networks. All though the Identity of a node can be verified through cryptographic authenticate, conventional security approaches are not always desirable because of their overhead requirements. In propose to use spatial information, a physical property associate with each node, hard to falsify, and not reliant on cryptographic the basis for 1) detecting spoofing attacks; 2) determining the number of attackers when multiple adversaries masquerading as the same node identity; and 3) localizing multiple adversaries. In propose to use the spatial correlation of received signal strength (RSS) inherited from wireless nodes to detect the spoofing attacks. Then formulate the problem of determining the number of attackers as a multiclass detection problem. Cluster based mechanism is developed to determine the number of attackers. When the training data are available, to explore using the Support Vector Machines (SVM) methods to further improve the accuracy of determining the number of attackers. In addition, to developed an integrated detection and localization system that can localize the positions of multiple attackers. The evaluated our techniques through two tested using both an 802.11 (Wi-Fi) network and an 802.15.4 (Zig- Bee) network in two real office buildings. Our experiment results show that our proposed methods can achieve over 90 percent Hit Rate and Precision when determining the number of attackers. Our localization results using a representative set of algorithms provide strong evidence of high accuracy of localizing multiple adversaries. The existing techniques are used to detect attackers but don’t know how it attacks. In this paper, we extend the RSS techniques to find out how attackers will attack by monitoring the attacker’s activities.