Intrusion Detection on Resource-Constrained IoT Devices with Hardware-Aware ML and DL
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918235606089728 |
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| author | Diab, Ali Chehade, Adel Ragusa, Edoardo Gastaldo, Paolo Zunino, Rodolfo Baghdadi, Amer Rizk, Mostafa |
| author_facet | Diab, Ali Chehade, Adel Ragusa, Edoardo Gastaldo, Paolo Zunino, Rodolfo Baghdadi, Amer Rizk, Mostafa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_02272 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Intrusion Detection on Resource-Constrained IoT Devices with Hardware-Aware ML and DL Diab, Ali Chehade, Adel Ragusa, Edoardo Gastaldo, Paolo Zunino, Rodolfo Baghdadi, Amer Rizk, Mostafa Networking and Internet Architecture Machine Learning 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. |
| title | Intrusion Detection on Resource-Constrained IoT Devices with Hardware-Aware ML and DL |
| topic | Networking and Internet Architecture Machine Learning |
| url | https://arxiv.org/abs/2512.02272 |