Network Intrusion Detection by Support Vectors and Ant Colony

This paper presents a framework for a new approach in intrusion detection by combining two existing machine learning methods (i.e. SVM and CSOACN). The IDS based on the new algorithm can be applied as pure SVM, pure CSOACN or their combination by constructing the detection classifier under three different training modes respectively. The initial experiments indicate that performance of their combination is better than pure SVM in terms of higher average detection rate as well as lower rates of both negative and positive false and is better than pure CSOACN in terms of less training time with comparable detection rate and false alarm rates.