A Sequential Hybrid Approach for Intrusion Detection System

As network attacks have increased in number and severity over the past few years, intrusion detection system (IDS) is increasingly becoming a critical component to secure the network. Due to large volumes of security audit data as well as complex and dynamic properties of intrusion behaviors, optimizing performance of IDS becomes an important open problem that is receiving more and more attention from the research community. Support Vector Machines (SVMs) are the classifiers which were originally designed for binary classification. The classification applications can solve multi-class problems. Decision-tree-based support vector machine which combines support vector machines and decision tree can be an effective way for solving multi-class problems. This method can decrease the training and testing time, increasing the efficiency of the system. In this paper we are studying an algorithm, A hierarchical binary tree multi-class support vector machine (BTMSVM), which has been used for classifying data. A BTMSVM based on class similarity in feature space is improved to overcome the drawbacks such as unclassifiable region which the existent methods have. This paper proposes the decision tree based algorithm to construct multiclass intrusion detection system.