A Machine Learning-Based Approach for Robust and Early Network Intrusion Detection

Abstract

Network Intrusion Detection Systems (NIDSs) using pattern matching have a fatal weakness in that they cannot detect new attacks because they only learn existing patterns and use them to detect those attacks. To solve this problem, a machine learning-based NIDS (ML-NIDS) that detects anomalies through ML algorithms by analyzing behaviors of protocols. However, the ML-NIDS learns the characteristics of attack traffic based on training data, so it, too, is inevitably vulnerable to attacks that have not been learned, just like pattern-matching machine learning. We want to presents a robust approach to detect intrusions early by analyzing representative features and detecting out-of-scope data. Experiments demonstrated that the proposed method effectively improves the robustness of existing ML-NIDS.