AI-based intelligent sensing detection of cybersecurity threats using multimodal sensor data in smart devices
Muhammad Latif, Abdul Ahad Abro, Syed Muhammad Daniyal, Abeer D. Algarni, Sadique Ahmad, Abdelhamied A. Ateya, Mohsin Mubeen Abbasi · Scientific Reports · 2026
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.