Abstract: Intrusion detection on Mobile Ad Hoc Networks (MANET) is a challenging task due to its unique characteristics such as open medium, dynamic topology, lack of centralized management and highly resource constrained nodes. Conventional Intrusion Detection System developed for wired networks cannot be directly applied to MANET. It needs to be redesigned to suit the ad hoc technology. Proposed IDS uses cross layer features instead of using single layer features to improve the performance. Also, the proposed system maximizes the detection accuracy by using two machine learning techniques. Support Vector Machines (SVM) and rough set theory are used together to take the advantage of better accuracy of SVM and faster speed of rough set. The performance of the system is validated using Network Simulator (NS2). The simulation results demonstrate that the proposed IDS effectively detect the anomalies with high detection accuracy.
T. Poongothai and K. Duraiswamy, 2013. Effective Cross Layer Intrusion Detection in Mobile Ad Hoc Networks Using Rough Set Theory and Support Vector Machines. Asian Journal of Information Technology, 12: 242-249.