Intrusion Detection System Using Binary Classifier Algorithm
Jeya. · i-manager’s Journal on Software Engineering · 2013
An Intrusion Detection System (IDS) is a security layer used to detect ongoing intrusive activities in information systems. Traditionally, intrusion detection relies on extensive knowledge of security experts, in particular, on their familiarity with the computer system to be protected. To reduce this dependence, various data-mining and machine learning techniques have been deployed for intrusion detection. An IDS is usually working in a dynamically changing environment, which forces continuous tuning of the intrusion detection model, in order to maintain sufficient performance. The manual tuning process required by current system depends on the system operators in working out the tuning solution and in integrating it into the detection model. In this paper, an Automatically Tuning IDS (ATIDS) is presented. The proposed system will automatically tune the detection model on-the-fly according to the feedback provided by the system operator when false predictions are encountered. The system is evaluated using the KDDCup'99 intrusion detection dataset. Key word: Attack Detection Model, Classification, Data Mining, Intrusion Detection, Artificial Intelligence.