Proposed Intrusion Detection on ZRP based MANET by effective k-means clustering method of data mining

Mobile Ad-Hoc Networks (MANET) consist of peer-to-peer infrastructure less communicating nodes that are highly dynamic. As a result, routing data becomes more challenging. Ultimately routing protocols for such networks face the challenges of random topology change, nature of the link (symmetric or asymmetric) and power requirement during data transmission. Under such circumstances both, proactive as well as reactive routing are usually inefficient. We consider, zone routing protocol (ZRP) that adds the qualities of the proactive (IARP) and reactive (IERP) protocols. In ZRP, an updated topological map of zone centered on each node, is maintained. Immediate routes are available inside each zone. In order to communicate outside a zone, a route discovery mechanism is employed. The local routing information of the zones helps in this route discovery procedure. In MANET security is always an issue. It is possible that a node can turn malicious and hamper the normal flow of packets in the MANET. In order to overcome such issue we have used a clustering technique to separate the nodes having intrusive behavior from normal behavior. We call this technique as effective k-means clustering which has been motivated from k-means. We propose to implement Intrusion Detection System on each node of the MANET which is using ZRP for packet flow. Then we will use effective k-means to separate the malicious nodes from the network. Thus, our Ad-Hoc network will be free from any malicious activity and normal flow of packets will be possible.