Intrusion Detection on Resource-Constrained IoT Devices with Hardware-Aware ML and DL

Abstract--This paper proposes a hardware-aware intrusion detection system (IDS) for Internet of Things (IoT) and Industrial IoT (IIoT) networks; it targets scenarios where classification is essential for fast, privacy-preserving, and resource-efficient threat detection. The goal is to optimize both tree-based machine learning (ML) models and compact deep neural networks (DNNs) within strict edge-device constraints. This allows for a fair comparison and reveals trade-offs between model families. We apply constrained grid search for tree-based classifiers and hardware-aware neural architecture search (HW-NAS) for 1D convolutional neural networks (1D-CNNs). Evaluation on the Edge-IIoTset benchmark shows that selected models meet tight flash, RAM, and compute limits: LightGBM achieves 95.3% accuracy using 75 KB flash and 1.2 K operations, while the HW-NAS-optimized CNN reaches 97.2% with 190 KB flash and 840 K floating-point operations (FLOPs). We deploy the full pipeline on a Raspberry Pi 3 B+, confirming that tree-based models operate within 30 ms and that CNNs remain suitable when accuracy outweighs latency. The widespread deployment of Internet of Things (IoT) systems has expanded the attack surface of modern networks, which now include critical infrastructure and operational environments vulnerable to advanced cyber threats [1], [2].