A Multistage Support Vector Machine Based Intrusion Detection System in MANET

Mobile Ad Hoc Networks (MANETs) have been applied in many different fields in recent years. Although MANETs are highly vulnerable to malicious behavior, complete security is complicated to achieve. Due to the insufficiency of prevention techniques, the Intrusion Detection System (IDS), which monitors system activity and detects intrusions, is generally used with other security measures. Denial of Service (DoS) type attacks such as flooding, blackhole, and grayhole attacks are acute types of network intrusion that aim to make computer/network resources unavailable to legitimate users. Intrusion Detection (ID) is a security management system that serves as an alarm mechanism for any computer network such as MANET. It detects the incoming security threats to a network and then issues an alarm message to an entity to take needed actions against the intrusion. An IDS gathers and examines information from numerous areas within a computer or a network to identify possible security breaches, including intrusions (attacks from outside the organization) and misuse (attacks from within the organization). The goal of this study is to develop a multistage ID technique for detecting flooding, blackhole, and gray-hole intrusions using Support Vector Machines (SVM). The SVM mechanism supports binary classification and separating data points into two classes. Hence, in this research SVM approach is used for classifying and detecting multiple attacks after breaking down the multiclassification problem into numerous binary classification problems. Keywords: Mobile Ad-hoc network, Support Vector Machine, On-demand Distance Vector, black hole, grayhole, flooding