Toward support-vector machine-based ant colony optimization algorithms for intrusion detection

One of the major challenges of network traffic analysis is intrusion detection. Intrusion detection systems (IDSs) are designed to detect malicious activities that attempt to compromise the confidentiality, integrity, and assurance of computer systems. Intrusion detection system has become the most widely employed security technology. The novelty of the proposed research is to develop a system for IDSs. In this research, a support-vector machine (SVM) with ant colony optimization (ACO) is proposed to detect an intrusion. Standard data sets, namely Knowledge Discovery and Data Mining (KDD) Cup '99 and Network Security Laboratory (NSL)-KDD, were utilized to test the results of the proposed system. One of the greatest challenges in a network analysis dataset is dimensionality. To handle dimensionality reduction, the ant colony optimization algorithm was applied. In the ACO method, significant subset features are selected from the entire dataset. These subset features have proceeded the SVM machine learning algorithm for detection intrusion. The empirical results point out that the SVM with ACO has obtained superior accuracy. It is concluded that the SVM-ACO model can more efficiently protect a network system from intrusion.