A Heirarichal OLSR Wireless Sensor Network Attack Detection using Machine Learning Approach

Network coding can greatly increase the efficiency of mobile ad hoc mobile network transmissions that are prone to delay, damage, and postponement, and jamming (NC). To correctly identify and isolate misbehaving nodes in enhanced intermediate node forwarding protocol, an IDS method based on communication that is End-to-End (E2E) between the source and the recipient is described. A group of nearby nodes work together to create and disseminate an attackers list so that other nodes can isolate bad actors by removing them from the routing table in order to make the right decisions. By removing the misbehaving node, the source can choose another reliable route to the location. The outcomes of the simulation shows the recommended strategy is effective and it can identify any number of attackers while consuming a very little amount of network bandwidth. It is possible to establish a social norm that is sustainable and maximizes social benefit by choosing the ideal design parameters, such as the network coding generation size, reputation update frequency, reputation threshold, and social strategy. Here, performance is examined in terms of packet loss rate and transmission patterns, as well as how these factors affect players' choices to adhere to the ideal social norm. Finally, practical difficulties are addressed, such as dispersed reputation propagation and the existence of both altruistic and malignant people.