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<p>The IoT has posed novel cyber-physical vulnerabilities due to the fast proliferation of Internet of Things (IoT) systems. Old network-based intrusion detection solutions can poorly identify malicious activities that are caused by on-device sensors. This paper introduces a multimodal sensing architecture based on deep learning to identify cyber-attacks on the traces of heterogeneous sensors, such as acceleration, gyroscopes, microphones, and temperature devices. The new hybrid CNN-RNN-Transformer architecture allows a fusion of features, as well as consideration of spatial-temporal interaction between sensor modalities. Evaluation was done using a manually annotated multimodal dataset and two publicly available benchmark datasets (CICIDS-2017 and IoT-23). The framework obtained an AUC of 0.96, an F1-score of 0.94, and an inference latency of 23 ms on edge hardware, and verified real-time deployability. These findings indicate that multimodal deep learning is a useful and scalable approach to cyber-physical threat detection in IoT settings that are resource-constrained.</p>

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AI-based intelligent sensing detection of cybersecurity threats using multimodal sensor data in smart devices

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月26日

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  • Muhammad Latif,
  • Abdul Ahad Abro,
  • Syed Muhammad Daniyal,
  • Abeer D. Algarni,
  • Sadique Ahmad,
  • Abdelhamied Ashraf Ateya,
  • Mohsin Mubeen Abbasi

摘要

The IoT has posed novel cyber-physical vulnerabilities due to the fast proliferation of Internet of Things (IoT) systems. Old network-based intrusion detection solutions can poorly identify malicious activities that are caused by on-device sensors. This paper introduces a multimodal sensing architecture based on deep learning to identify cyber-attacks on the traces of heterogeneous sensors, such as acceleration, gyroscopes, microphones, and temperature devices. The new hybrid CNN-RNN-Transformer architecture allows a fusion of features, as well as consideration of spatial-temporal interaction between sensor modalities. Evaluation was done using a manually annotated multimodal dataset and two publicly available benchmark datasets (CICIDS-2017 and IoT-23). The framework obtained an AUC of 0.96, an F1-score of 0.94, and an inference latency of 23 ms on edge hardware, and verified real-time deployability. These findings indicate that multimodal deep learning is a useful and scalable approach to cyber-physical threat detection in IoT settings that are resource-constrained.