Battery-drain attacks pose a stealthy yet critical threat to Internet of Things (IoT) networks, enabling adversaries to exhaust device power without triggering traffic-based alerts. Existing intrusion detection systems (IDS) seldom address such energy-aware threats or report false alarm rates under realistic conditions. This paper introduces the Multi-View Energy-Aware Intrusion Detection System (MV-EA-IDS), a dual-view framework combining network-level analysis with inferred Energy Proxy Features to detect both conventional and power-drain attacks. It integrates a supervised Random Forest for traffic classification and an unsupervised Isolation Forest trained on benign energy proxies to capture anomalous drain patterns. Adaptive percentile thresholds and a meta-heuristic k-of-n fusion rule further minimize false positives. On the public CIC-IoT2023 dataset, MV-EA-IDS achieves 99.87% accuracy and only 0.01% false positives, establishing the first reproducible benchmark for energy-aware IoT intrusion detection.
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Rahma Berchi
Lemia Louail
Sarra Cherbal
Centre National de la Recherche Scientifique
Université de Lorraine
University of Technology Malaysia
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Berchi et al. (Tue,) studied this question.
www.synapsesocial.com/papers/69a765bfbadf0bb9e87da47e — DOI: https://doi.org/10.1109/icaaid68975.2025.11358086
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