Understanding the AI-powered Binary Code Similarity Detection
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910643730251776 |
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| author | Fu, Lirong Liu, Peiyu Meng, Wenlong Lu, Kangjie Zhou, Shize Zhang, Xuhong Chen, Wenzhi Ji, Shouling |
| author_facet | Fu, Lirong Liu, Peiyu Meng, Wenlong Lu, Kangjie Zhou, Shize Zhang, Xuhong Chen, Wenzhi Ji, Shouling |
| contents | AI-powered binary code similarity detection (BinSD), which transforms intricate binary code comparison to the distance measure of code embedding through neural networks, has been widely applied to program analysis. However, due to the diversity of the adopted embedding strategies, evaluation methodologies, running environments, and/or benchmarks, it is difficult to quantitatively understand to what extent the BinSD problem has been solved, especially in realworld applications. Moreover, the lack of an in-depth investigation of the increasingly complex embedding neural networks and various evaluation methodologies has become the key factor hindering the development of AI-powered BinSD. To fill these research gaps, in this paper, we present a systematic evaluation of state-of-the-art AI-powered BinSD approaches by conducting a comprehensive comparison of BinSD systems on similar function detection and two downstream applications, namely vulnerability search and license violation detection. Building upon this evaluation, we perform the first investigation of embedding neural networks and evaluation methodologies. The experimental results yield several findings, which provide valuable insights in the BinSD domain, including (1) despite the GNN-based BinSD systems currently achieving the best performance in similar function detection, there still exists considerable space for improvements;(2) the capability of AI-powered BinSD approaches exhibits significant variation when applied to different downstream applications;(3) existing evaluation methodologies still need substantial adjustments. For instance, the evaluation metrics (such as the widely adopted ROC and AUC) usually fall short of accurately representing the model performance of the practical use in realworld scenarios. Based on the extensive experiments and analysis, we further provide several promising future research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07537 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Understanding the AI-powered Binary Code Similarity Detection Fu, Lirong Liu, Peiyu Meng, Wenlong Lu, Kangjie Zhou, Shize Zhang, Xuhong Chen, Wenzhi Ji, Shouling Software Engineering AI-powered binary code similarity detection (BinSD), which transforms intricate binary code comparison to the distance measure of code embedding through neural networks, has been widely applied to program analysis. However, due to the diversity of the adopted embedding strategies, evaluation methodologies, running environments, and/or benchmarks, it is difficult to quantitatively understand to what extent the BinSD problem has been solved, especially in realworld applications. Moreover, the lack of an in-depth investigation of the increasingly complex embedding neural networks and various evaluation methodologies has become the key factor hindering the development of AI-powered BinSD. To fill these research gaps, in this paper, we present a systematic evaluation of state-of-the-art AI-powered BinSD approaches by conducting a comprehensive comparison of BinSD systems on similar function detection and two downstream applications, namely vulnerability search and license violation detection. Building upon this evaluation, we perform the first investigation of embedding neural networks and evaluation methodologies. The experimental results yield several findings, which provide valuable insights in the BinSD domain, including (1) despite the GNN-based BinSD systems currently achieving the best performance in similar function detection, there still exists considerable space for improvements;(2) the capability of AI-powered BinSD approaches exhibits significant variation when applied to different downstream applications;(3) existing evaluation methodologies still need substantial adjustments. For instance, the evaluation metrics (such as the widely adopted ROC and AUC) usually fall short of accurately representing the model performance of the practical use in realworld scenarios. Based on the extensive experiments and analysis, we further provide several promising future research directions. |
| title | Understanding the AI-powered Binary Code Similarity Detection |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2410.07537 |