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Enhancing IoMT Security Using Large Multimodal Models
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AI
DOI：10.1109/MNET.2025.3545692.png)
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Abstract
En 中文
This paper explores the potential of Large Multimodal Models (LMMs) in enhancing the security of Internet of Medical Things (IoMT) networks. As IoMT systems become more complex and generate vast amounts of sensitive data, traditional security measures are increasingly insufficient to address evolving cyber threats. The ability of LMMs to process and integrate diverse data types, such as sensor data, audio, and video, offers a powerful approach to detecting and mitigating security risks in real time. By focusing on key applications like anomaly detection through traffic prediction and improving Physical Layer Security (PLS), this work demonstrates how LMMs can significantly enhance the resilience of IoMT networks against a wide range of cyber threats. The integration of multimodal data enables more accurate traffic predictions and allows adaptive responses to emerging security challenges. Additionally, LMMs contribute to strengthening PLS by identifying vulnerabilities in communication channels and optimizing resource allocation. Through dynamic data fusion techniques, LMMs ensure that IoMT systems can respond to threats in real-time, while maintaining high security and minimizing impact on system performance.
Keywords:
Internet of Medical Things
Large Multimodal Models
IoMT
LMMs
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