A Hybrid Classification Framework for Network Intrusion Detection with High Accuracy and Low Latency

Abstract

Network intrusion detection (NIDS) is a crucial task aimed at safeguarding computer networks against malicious attacks. Traditional NIDS methods can be categorized as either misuse-based or anomaly-based, each having its unique set of limitations. Misuse-based approaches excel in identifying known attacks but fall short when dealing with new or unidentified attack patterns. On the other hand, anomaly-based methods are more adept at identifying novel attacks but tend to produce a substantial number of false positives. To enhance the overall performance of NIDS systems, hybrid classification techniques are employed, leveraging the strengths of both misuse-based and anomaly-based methods. In this research, we present a novel hybrid classification approach for NIDS that excels in both speed and accuracy. Our approach integrates a blend of machine learning algorithms, including decision trees, support vector machines, and deep neural networks. We conducted comprehensive evaluations of our approach using various network intrusion datasets, achieving state-of-the-art results in terms of accuracy and prediction speed.