Network Intrusion Detection using Support Vector Regression

Data Mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in databases process. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. This paper addresses using ensemble approach of Support Vector Regression for intrusion detection. Due to increasing incidents of cyber attacks, building effective intrusion detection systems (IDS) are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: Network Intrusion Detection. Intrusion detection systems help network administrators prepare for and deal with network security attacks. These systems collect information from a variety of systems and network sources, and analyze them for signs of intrusion and misuse. We show that proposed ensemble of Support Vector Regression is superior to individual approach for intrusion detection in terms of classification rate.