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arXiv · 2605.04407

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection

Md Zakir Hossain·Md Ayshik Rahman Khan·Md Rafiqul Islam·Syed Mohammed Shamsul Islam·Tom Gedeon
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Abstract

The growth of networked and IoT systems has intensified cyber-security threats and exposed the limits of traditional signature-based intrusion detection. Although machine-learning-based intrusion detection systems often report strong benchmark performance, high ac- curacy within a single dataset does not necessarily guarantee reliable performance in unseen network environments. This study investigates the generalisation capability of supervised machine learning models for intrusion detection using UNSW-NB15 and TON_IoT. Random Forest, Logistic Regression, and Naive Bayes were evaluated under same-dataset and cross-dataset settings. Random Forest achieved the strongest same dataset performance, with 95.08% accuracy on UNSW-NB15 and 99.79% on TON_IoT, but performance dropped sharply in cross-dataset testing. When trained on UNSW-NB15 and tested on TON_IoT or vice versa, below 40% accuracy. These results reveal a significant generalisation gap in intrusion detection. We connect this challenge to affective computing and human-centric AI, where behavioural signal analysis, anomaly detection, domain shift, and context-sensitive modelling are also central. This framing highlights the need for adaptive, generalisable cyber-security models that can operate across changing network and IoT environments.

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Md Zakir Hossain, Md Ayshik Rahman Khan, Md Rafiqul Islam, Syed Mohammed Shamsul Islam, Tom Gedeon. 2026-05-06. Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection. https://arxiv.org/abs/2605.04407

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