EVALUATING NETWORK INTRUSION DETECTION SYSTEMS

Network security in the current generation is one of the most crucial security aspects. With the recent advances in computer technology and recent dependency of the same on networks, it is important to ensure the secure working of network communications. Intrusion detection system keeps track of network communications for malicious behavior. The most developed Intrusion Detection System is Signature-based system. Even though network security and intrusion detection systems have been the top areas of research, we face enormous amounts of threats. In this paper we address the downside of existing Signature-based systems as well as look into the advances in the research on how Machine Learning algorithms are being applied to ensure maximum network security. We compare the accuracies of machine learning models for network intrusion detection systems and analyze the reason for it's failure. The references cited cover the major theoretical issues. This paper also talks about how the combination of Neural Networks and Anomaly Detection could help improve the accuracy. Keywords - Intrusion Detection Systems, Network Security, Anomaly Detection, Machine Learning, Firewall