Secondary Server Based Packet Dropping Attack Detection Using Modified Support Vector Machine

Mobile Adhoc networks are mainly affected by one of the prominent attacks namely Packet dropping attacks which in turn reduces packet delivery ratio affecting network performance. Distributed and Cooperative Intrusion Detection System (DC-IDS) is greatly involved in earlier researches for these attacks mitigation. The packet forwarding proficiency is accomplished by selfish behavior of nodes prediction through monitoring but it fails to endeavor compromised node behavior detection and tends to up surge computational overhead. Secondary Server based Distributed and Cooperative Intrusion Detection System (SS-DC-IDS) is presented in this research to alleviate the above issues. This research work also greatly involves Secondary server selection primarily for intrusion attack detection improvement and mitigating primary server computation overhead. Animal migration algorithm with the consideration fitness values energy and bandwidth are utilized for secondary server selection. The cluster head selection through clustering algorithm in which fragmenting large network environment into small organization is accomplished. Hybrid side channel monitoring techniques play a vital role in data transmission behavior monitoring via cluster head collecting the attack features from the cluster members. The secondary server receives the collected features before data transmission. Modified Support Vector Machine algorithm is mainly involved in information learning Secondary server and checks whether intrusion attacks present or not. NS2 simulation environment is eminently utilized for the analysis of research work which evidently presents improved performance than the prevailing research work