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Leveraging Semantic Relations in Code and Data to Enhance Taint Analysis of Embedded Systems

Authors: Zhao, Jiaxu and Li, Yuekang and Zou, Yanyan and Liang, Zhaohui and Xiao, Yang and Li, Yeting and Peng, Bingwei and Zhong, Nanyu and Wang, Xinyi and Wang, Wei and others

Abstract:

IoT devices have significantly impacted our daily lives, and detecting vulnerabilities in embedded systems early on is critical for ensuring their security. Among the existing vulnerability detection techniques for embedded systems, static taint analysis has been proven effective in detecting severe vulnerabilities, such as command injection vulnerabilities, which can cause remote code execution. Nevertheless, static taint analysis is faced with the problem of identifying sources comprehensively and accurately.

Link: Read Paper

Labels: static analysis, bug detection, code model, code model training, source code model