MACHINE LEARNING FOR NETWORK INTRUSION DETECTION AND RELIABLE SECURITY EVALUATION

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Abstract

Machine learning can assist network intrusion detection by classifying observed activity and prioritizing unusual behavior for investigation. Its operational value depends on the quality of the traffic representation, the validity of training labels, and the conditions under which performance is measured. This narrative review examines foundational methodological research, two established benchmark datasets, and an online autoencoder approach. It develops a conceptual evaluation framework for a resource constrained institutional network. The framework separates predictive discrimination from false alert burden, detection delay, and the ability to generalize across time and environments. It also distinguishes unusual behavior from confirmed malicious activity. Aproposed pilot compares a supervised baseline, a tree ensemble, and an anomaly detector using controlled data partitions and a fixed alert budget. No new dataset, implementation, or experimental result is presented. The analysis concludes that credible evaluation requires leakage resistant partitioning, realistic estimates of attack prevalence, transparent feature availability, and analyst review. Ahigh benchmark accuracy alone cannot establish readiness for operational deployment.

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