Author : Meesala Shobha Rani and S. Basil Xavier

Cyber security threats have become increasingly sophisticated and complex. Intrusion detection which is one of the main problems in computer security has the main goal to detect infrequent access or attacks and to protect internal networks. A new hybrid intrusion detection method combining multiple classifiers for classifying anomalous and normal activities in the computer network is presented. The misuse detection model is built based on the C5.0 Decision tree algorithm and using the information collected anomaly detection model is built which is implemented by one-class Support Vector Machine (SVM). Integration of multiple algorithms helps to get better performance. The Experimental results are performed on NSL-KDD Dataset, and it is shown that overall performance of the proposed approach is improved in terms of detection rate and low false alarms rate in comparison to the existing techniques.

Keywords: Intrusion detection system, Misuse detection, Anomaly detection, hybrid approach, C5.0 Decision tree, One Class SVM.

Article published in International Journal of Current Engineering and Technology, Vol.5, No.3 (June-2015)