A behavioral model to identify and analyze anomalous attacks on packet forwarding

Data pass in any network system through packets to perform communication. On these packets, an intruder may try to perform eavesdropping that leads to information loss. Generally, Packet dropping and modification are common attacks that can be launched by an adversary to disrupt communication in multi hop networks. To compensate such data loss, many schemes have been proposed to identify such attacks. Out of which very few techniques were able to identify such type of intruders in an effective manner. A systematic approach is proposed to locate and identify packet droppers and modifiers. Hence it has been assumed that the nodes continuously monitor the forwarding behaviors of their neighbors to determine if their neighbors are misbehaving. To address this problem, an effective method is designed which can identify misbehaving forwarders that drop or modify packet with a hierarchical structure. The scheme is focused towards behavioral study of the nodes for the detection of attacks in the network. Extensive analysis and simulations have been conducted to study the performance of attacks with respect to efficiency of the scheme.