A novel clustering approach and adaptive SVM classifier for intrusion detection in WSN: A data mining concept

Abstract

Nowadays Wireless Sensor Network (WSN) mainly faces security issue during packet transmission between different sensor nodes in network combined with data mining. To overcome this challenge an efficient clustering technique called adaptive chicken swarm optimization algorithm is proposed for cluster head (CH) selection. By this adaptive method the time consumption is reduced to a greater extend along with that the lifetime of the network and the scalability is improved alternatively. Additionally a two stage classification technique known as adaptive SVM classification a supervised learning technique is proposed with Intrusion Detection System (IDS) where an acknowledgement based method is utilized for reporting the malicious sensor nodes. By this acknowledgement different types of attacks such as DOS, probe, U2R, R2L are detected incorporation with Intrusion Detection System (IDS). Once detected a high level security mechanism along with intrusion response is provided to other sensor nodes by which a secure packet transmission occurs between different sensor nodes. The proposed methodology is implemented in python platform and the comparison results provided with existing methods proves a better result.