Implement Dynamic Approach for Anomaly Detection in AODV for Manet

Abstract

 Due to their unique characteristics, such as the dynamic changing topology, the absence of central management, the cooperative routing mechanisms, and the resources constraints, Mobile ad hoc networks (MANETs) are relatively vulnerable to both active and passive attacks. In MANET, routing attacks try to disrupt the functions of routing protocol by intentionally or unintentionally dropping packets or propagating faked routing messages. However, due to their computation requirements, the prevention mechanisms are not powerful enough to secure MANET. 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.