A study on classification techniques for network intrusion detection

Computer systems vulnerabilities such as software bugs are often exploited by malicious users to intrude into information systems. With the recent growth of the Internet such security limitations are becoming more and more pressing. One commonly used defense measure against such malicious attacks in the Internet are Intrusion Detection Systems (IDSs). In this paper, we compare the ability of three classification techniques (k-means classifiers, neural networks and support vector machines) to perform for network intrusion detection applications. The results indicate that Support Vector Machines train in the shortest amount of time with an acceptable accuracy whilst Neural Networks exhibit high accuracy at the cost of long training times.