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<p>Deploying effective intrusion detection systems on low-power IoT devices requires a careful balance of performance, efficiency, and transparency. Current neural-network-based solutions are often too resource-intensive and opaque for practical edge use. We introduce a lightweight, explainable IDS that combines a 1D–CNN for spatial feature analysis with SHAP for model interpretation. To the best of our knowledge, this work presents the first comparative analysis of a SHAP-augmented 1D–CNN against traditional CNN and LSTM models for IoT intrusion detection. Our method outperforms these benchmarks on real-world datasets (UNSW–NB15 and WUSTL–IIoT–2021). Crucially, SHAP analysis enables feature reduction, yielding streamlined models that preserve over 93% F1-score and reduce computational overhead by more than 38%, facilitating millisecond-level inference on edge hardware. These results demonstrate a viable path for reconciling high detection accuracy with the stringent resource limitations of IoT environments.</p>

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Enhancing IoT network security with explainable deep learning-based intrusion detection systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月16日

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  • Miracle Udurume,
  • Vladimir Shakhov,
  • Insoo Koo

摘要

Deploying effective intrusion detection systems on low-power IoT devices requires a careful balance of performance, efficiency, and transparency. Current neural-network-based solutions are often too resource-intensive and opaque for practical edge use. We introduce a lightweight, explainable IDS that combines a 1D–CNN for spatial feature analysis with SHAP for model interpretation. To the best of our knowledge, this work presents the first comparative analysis of a SHAP-augmented 1D–CNN against traditional CNN and LSTM models for IoT intrusion detection. Our method outperforms these benchmarks on real-world datasets (UNSW–NB15 and WUSTL–IIoT–2021). Crucially, SHAP analysis enables feature reduction, yielding streamlined models that preserve over 93% F1-score and reduce computational overhead by more than 38%, facilitating millisecond-level inference on edge hardware. These results demonstrate a viable path for reconciling high detection accuracy with the stringent resource limitations of IoT environments.