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

• URL: http://arxiv.org/abs/2512.02272v1
• Date: Mon, 01 Dec 2025 23:36:03 GMT
• Title: Intrusion Detection on Resource-Constrained IoT Devices with Hardware-Aware ML and DL
• Authors: Ali Diab, Adel Chehade, Edoardo Ragusa, Paolo Gastaldo, Rodolfo Zunino, Amer Baghdadi, Mostafa Rizk,
• Abstract summary: This paper proposes a hardware-aware intrusion detection system (IDS) for the Internet of Things (IoT) and Industrial IoT (IIoT) networks.<n>It targets scenarios where classification is essential for fast, privacy-preserving, and resource-efficient threat detection.<n>The goal is to optimize both tree-based machine learning (ML) models and compact deep neural networks (DNNs) within strict edge-device constraints.
• Score: 3.218984853261389
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 Plus, confirming that tree-based models operate within 30 ms and that CNNs remain suitable when accuracy outweighs latency. These results highlight the practicality of hardware-constrained model design for real-time IDS at the edge.

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