Visible to the public Cluster based Intrusion Detection System for Mobile Ad-hoc Network

TitleCluster based Intrusion Detection System for Mobile Ad-hoc Network
Publication TypeConference Paper
Year of Publication2019
AuthorsGopalakrishnan, S., Rajesh, A.
Conference Name2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM)
Keywordsactive monitoring node, Ad Hoc Network Security, ad-hoc node security, CBIDP, cluster, cluster based Intrusion detection planning, clustering protocols, compositionality, DRINA Intrusion, Intrusion detection, malicious node, MANET, MANET Attack Detection, memory overhead, Metrics, mobile ad hoc networks, mobile ad-hoc network, mobility management (mobile radio), Monitoring, monitoring process, network securities, network traffics, node mobility, pattern clustering, pubcrawl, Resiliency, route structure, Routing Protocol, Routing protocols, security of data, Voting

Mobile Ad-hoc network is decentralized and composed of various individual devices for communicating with each other. Its distributed nature and infrastructure deficiency are the way for various attacks in the network. On implementing Intrusion detection systems (IDS) in ad-hoc node securities were enhanced by means of auditing and monitoring process. This system is composed with clustering protocols which are highly effective in finding the intrusions with minimal computation cost on power and overhead. The existing protocols were linked with the routes, which are not prominent in detecting intrusions. The poor route structure and route renewal affect the cluster hardly. By which the cluster are unstable and results in maximization processing along with network traffics. Generally, the ad hoc networks are structured with battery and rely on power limitation. It needs an active monitoring node for detecting and responding quickly against the intrusions. It can be attained only if the clusters are strong with extensive sustaining capability. Whenever the cluster changes the routes also change and the prominent processing of achieving intrusion detection will not be possible. This raises the need of enhanced clustering algorithm which solved these drawbacks and ensures the network securities in all manner. We proposed CBIDP (cluster based Intrusion detection planning) an effective clustering algorithm which is ahead of the existing routing protocol. It is persistently irrespective of routes which monitor the intrusion perfectly. This simplified clustering methodology achieves high detecting rates on intrusion with low processing as well as memory overhead. As it is irrespective of the routes, it also overcomes the other drawbacks like traffics, connections and node mobility on the network. The individual nodes in the network are not operative on finding the intrusion or malicious node, it can be achieved by collaborating the clustering with the system.

Citation Keygopalakrishnan_cluster_2019