Anomaly-Based Network Intrusion Detection Using SVM

Abstract

With the rapid development of new technologies and applications of Internet, much attention has been paid to the detection of anomalies in cyberspace traffic. A series of intrusion detection techniques based on machine learning have been developed. Support vector machine (SVM), as an essential approach, has been paid close attention in this filed. Nevertheless, the existing SVM-based techniques with the training features can not efficiently detect short duration intrusions and attacks in the traffic. To tackle this issue, we propose an anomaly-based SVM detection scheme by extracting and optimizing the training features. It trains the SVM with Kullback-Leibler (KL) divergence and cross-correlation calculated by the control and data planes traffic. Following this way, the novel training method can effectively enhance the detection accuracy. And the performance of the presented scheme is validated and evaluated based on a recent realistic Internet traffic dataset. Finally, relevant results indicate that the developed method establishes the relationship between Transmission Control Protocol (TCP) traffic and intrusions. It can efficiently detect short duration intrusions and attacks in the network traffic.