A Dynamic Approach for Anomaly Detection in AODV

Mobile ad hoc networks (MANETs) are relatively vulnerable to malicious network attacks, and therefore, security is a more significant issue than infrastructure-based wire-less networks. In MANETs, it is difficult to identify malicious hosts as the topology of the network dynamically changes. A malicious host can easily interrupt a route for which it is one of the forming nodes in the communication path. Since the topology of a MANET dynamically changes, the mere use of a static baseline profile is not efficient. We proposed a new anomaly-detection scheme based on a dynamic learning process that allows the training data to be updated at particular time intervals. Our dynamic learning process involves calculating the projection distances based on multidimensional statistics using weighted coefficients and a forgetting curve.