Interpretation-enabled Software Reuse Detection Based on a Multi-Level Birthmark Model
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A4 Artikkeli konferenssijulkaisussa
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en
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12
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Proceedings of 43rd IEEE/ACM International Conference on Software Engineering, ICSE 2021, pp. 873-884, Proceedings - International Conference on Software Engineering
Abstract
Software reuse, especially partial reuse, poses legal and security threats to software development. Since its source codes are usually unavailable, software reuse is hard to be detected with interpretation. On the other hand, current approaches suffer from poor detection accuracy and efficiency, far from satisfying practical demands. To tackle these problems, in this paper, we propose ISRD, an interpretation-enabled software reuse detection approach based on a multi-level birthmark model that contains function level, basic block level, and instruction level. To overcome obfuscation caused by cross-compilation, we represent function semantics with Minimum Branch Path (MBP) and perform normalization to extract core semantics of instructions. For efficiently detecting reused functions, a process for “intent search based on anchor recognition” is designed to speed up reuse detection. It uses strict instruction match and identical library call invocation check to find anchor functions (in short anchors) and then traverses neighbors of the anchors to explore potentially matched function pairs. Extensive experiments based on two real-world binary datasets reveal that ISRD is interpretable, effective, and efficient, which achieves 97.2% precision and 94. 8% recall. Moreover, it is resilient to cross-compilation, outperforming state-of-the-art approaches.Description
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Xu, X, Zheng, Q, Yan, Z, Fan, M, Jia, A & Liu, T 2021, Interpretation-enabled Software Reuse Detection Based on a Multi-Level Birthmark Model. in Proceedings of 43rd IEEE/ACM International Conference on Software Engineering, ICSE 2021. Proceedings - International Conference on Software Engineering, IEEE, pp. 873-884, International Conference on Software Engineering, Virtual, Online, Spain, 25/05/2021. https://doi.org/10.1109/ICSE43902.2021.00084