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LibAM: An Area Matching Framework for Detecting Third-party Libraries in Binaries
LibAM is the first approach capable of detecting the exact reuse areas on FCG and offering substantial benefits for downstream tasks, as well as evaluating LibAM’s accuracy on large-scale, real-world binaries in IoT firmware and generating a list of potential vulnerabilities for these devices.
Siyuan Li
,
Yongpan Wang
,
Chaopeng Dong
,
Shouguo Yang
,
Hong Li
,
Hao Sun
,
Zhe Lang
,
Zuxin Chen
,
Weijie Wang
,
Hongsong Zhu
,
Limin Sun
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Asteria-Pro: Enhancing Deep-Learning Based Binary Code Similarity Detection by Incorporating Domain Knowledge
A novel deep learning enhancement architecture by incorporating domain knowledge-based pre-filtration and re-ranking modules is proposed, and a prototype named Asteria-Pro based on Asteria is developed, which outperforms existing state-of-the-art approaches in the bug search task by a significant margin.
Shouguo Yang
,
Chaopeng Dong
,
Yang Xiao
,
Yiran Cheng
,
Zhiqiang Shi
,
Zhi Li
,
Limin Sun
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