CRADS: Integrated Cross Layer Approach for Detecting Routing Attacks in MANETs

In ad hoc networks, the vulnerability of nodes to routing attacks is a serious concern. In this work we propose a cross-layer based routing attack detection system for ad hoc networks. Previous work that uses mostly audit trails collected from the routing protocol suffers from inadequacy of features to construct a reliable model for detecting anomalous routing behavior. On the other hand, use of linear detectors lead to very high false positives and false negatives because of the inherent non-linear nature of the feature space. In this work, we address these issues by collating features from multiple protocols at different layers and using a non-linear detector based on support vector machine (SVM). The consequent problem of computational expense of the detection process is addressed by a combination of novel data reduction techniques. Simulation results show that the performance of the proposed CRADS is far superior than conventional protocol-specific detection systems.