为资源受限的物联网设备设计硬件感知的入侵检测系统，兼顾精度与效率。
Intrusion Detection on Resource-Constrained IoT Devices with Hardware-Aware ML and DL
- 针对边缘设备优化树模型和轻量卷积网络，适配存储与计算限制。
- 轻量级模型在边缘数据集上达95.3%~97.2%准确率，内存占用低于200KB。
- 适合需实时防护、隐私敏感的物联网/工业物联网场景部署。
本文提出一种面向物联网（IoT）与工业物联网（IIoT）网络的硬件感知入侵检测系统（IDS），旨在实现快速、隐私保护且资源高效的威胁检测。通过在严格边缘设备约束下优化树模型与紧凑深度神经网络（DNNs），实现两类模型的公平比较并揭示其权衡。采用约束网格搜索优化树分类器，结合硬件感知神经架构搜索（HW-NAS）优化一维卷积神经网络（1D-CNNs）。在Edge-IIoTset基准测试中，所选模型满足严苛的闪存、内存与算力限制：LightGBM 达到95.3%准确率，仅需75 KB闪存与1.2千次操作；经HW-NAS优化的CNN实现97.2%准确率，使用190 KB闪存与840 K FLOPs。在Raspberry Pi 3 B Plus上完整部署验证，树模型延迟低于30毫秒，而CNN在高精度需求场景仍适用。结果表明，硬件约束下的模型设计对边缘实时入侵检测具有实际可行性。
原文摘要 · Abstract (English)
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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