On the Cross-Dataset Generalization of Machine Learning for Network Intrusion Detection

arXiv:2402.10974v1 Announce Type: new
Abstract: Network Intrusion Detection Systems (NIDS) are a fundamental tool in cybersecurity. Their ability to generalize across diverse networks is a critical factor in their effectiveness and a prerequisite for real-world applications. In this study, we conduct a comprehensive analysis on the generalization of machine-learning-based NIDS through an extensive experimentation in a cross-dataset framework. We employ four machine learning classifiers and utilize four datasets acquired from different networks: CIC-IDS-2017, CSE-CIC-IDS2018, LycoS-IDS2017, and LycoS-Unicas-IDS2018. Notably, the last …

analysis applications arxiv critical cs.cr cs.lg cs.ni cybersecurity dataset detection factor intrusion intrusion detection machine machine learning network networks nids real study systems tool world