Assessing the Robustness of Machine Learning Models for Wireless Intrusion Detection Under Adversarial Traffic Perturbations - Siddhant Sukhatankar (International Journal of Wireless Security and Networks, 2026)
As the Internet of Things (IoT) devices and wireless communication networks continue to grow rapidly, protecting systems from cyber threats has become increasingly important. Machine learning–based intrusion detection systems (IDS) have shown strong potential in detecting abnormal and malicious network activities, yet their effectiveness and resilience when facing adversarial attacks are still not sufficiently explored. This research evaluates Machine Learning (ML) models–XGBoost, random forest, and multi-layer perceptron (MLP)—in detecting attacks within wireless IoT networks when subjected to certain specific traffic feature perturbations. Using the CICIoT2023 and IoT intrusion datasets, we conducted binary classification experiments distinguishing benign and attack traffic. Perturbations simulating realistic adversarial manipulations were applied to numeric features at multiple levels (5%, 10%, and 25%). The results demonstrate that tree-based models, XGBoost, and random forest maintain high recall under perturbation, with less than 0.2% reduction at even the highest perturbation levels, whereas MLP performance is unstable on imbalanced data. Feature importance analysis reveals that timing and protocol-related features contribute significantly to model predictions. These findings highlight the robustness of ensemble tree methods in practical IoT intrusion detection scenarios and emphasize the need for perturbation-aware evaluations for ML-based IDS. The study contributes to safer deployment of wireless IDS and provides a methodological framework for assessing model resilience against adversarial feature modifications.
Keywords: Adversarial perturbations; IoT security; Machine Learning; XGBoost; random forest; wireless intrusion detection