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29 November 2023 Research on automated anomaly localization in the power Internet of Things based on fuzzing and semantic analysis
Gaozhou Wang, Wenbin Zhang, Li Yan, Linlin Tang, Haipeng Qu, Ke Liu, Yu Zhao, Haoran Li
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Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 129370Y (2023) https://doi.org/10.1117/12.3013357
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore
Abstract
The power IoT network serves as a critical social function in modern society but has also become a high-value target for malicious attackers. Therefore, it is of vital importance to rapidly discover vulnerabilities in power IoT devices and locate abnormal points for repairs in order to enhance the overall security of the power system. This paper presents the implementation of a prototype system called FuzzSem-AL for automated anomaly localization in power IoT devices. By utilizing deep learning-based semantic analysis techniques, the system recovers function names from the binary programs of power IoT devices and associates these functions with the communication protocol fields of the devices. Additionally, state backtracking techniques are employed to effectively eliminate the influence of non-root cause factors and pinpoint the root protocol fields that cause device crashes. Compared to existing techniques, this paper effectively combines the processes of fuzzing and anomaly localization, enabling automated identification of the core functions that trigger anomalies. The validation of the approach is conducted using past vulnerabilities in commonly used protocols in power IoT systems, such as FTP and SMTP. Ultimately, the paper successfully locates the function positions of 11 abnormal points out of 15 1day vulnerabilities.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Gaozhou Wang, Wenbin Zhang, Li Yan, Linlin Tang, Haipeng Qu, Ke Liu, Yu Zhao, and Haoran Li "Research on automated anomaly localization in the power Internet of Things based on fuzzing and semantic analysis", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 129370Y (29 November 2023); https://doi.org/10.1117/12.3013357
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KEYWORDS
Internet of things
Machine learning
Binary data
Information security
Network security
Deep learning
Education and training
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