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. The authors proposed a new anomaly-detection scheme based on a dynamic learning process that allows the training data to be updated at particular time intervals.