Abstract
IoT binary similarity detection is a way to determine whether two IoT components have a homology relationship. It is used to address security concerns arising from the reuse of open source components in the IoT software supply chain. In order to solve the problems caused by different architectures and different optimization levels during compilation, we propose a graph neural network based similarity assessment model for IoT binary components. We introduce an attention mechanism to get graph-level embedding based on GraphSAGE node-level embedding extraction, witch considers the importance of function nodes. Through comparative experiments with other models, this model shows better performance with 96.96% accuracy.
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Acknowledgements
This work is supported by Graduate Education Innovation and Quality Improvement Action Plan project of Henan University (No. SYLJD2022008 and No. SYLKC2022028), 2022 Discipline Innovation Introduction Base cultivation project of Henan University and Key Technology Research and Development Project of Henan Province under Grant 222102210055.
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Wang, Z., Hu, X., Zuo, F., Li, H., Zhang, Y., Wang, W. (2022). A Graph Neural Network Based Model for IoT Binary Components Similarity Detection. In: Ma, H., Wang, X., Cheng, L., Cui, L., Liu, L., Zeng, A. (eds) Wireless Sensor Networks. CWSN 2022. Communications in Computer and Information Science, vol 1715. Springer, Singapore. https://doi.org/10.1007/978-981-19-8350-4_10
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