计算机科学
可达性
编译程序
钥匙(锁)
仿真
杠杆(统计)
二进制数
代码覆盖率
编码(集合论)
相似性(几何)
程序设计语言
算法
理论计算机科学
软件
操作系统
算术
人工智能
数学
图像(数学)
经济
集合(抽象数据类型)
经济增长
作者
A. Zhou,Yikun Hu,Xiangzhe Xu,Charles Zhang
摘要
Binary code similarity analysis is extremely useful, since it provides rich information about an unknown binary, such as revealing its functionality and identifying reused libraries. Robust binary similarity analysis is challenging, as heavy compiler optimizations can make semantically similar binaries have gigantic syntactic differences. Unfortunately, existing semantic-based methods still suffer from either incomplete coverage or low accuracy. In this article, we propose ARCTURUS , a new technique that can achieve high code coverage and high accuracy simultaneously by manipulating program execution under the guidance of code reachability. Our key insight is that the compiler must preserve program semantics (e.g., dependences between code fragments) during compilation; therefore, the code reachability, which implies the interdependence between code, is invariant across code transformations. Based on the above insight, our key idea is to leverage the stability of code reachability to manipulate the program execution such that deep code logic can also be covered in a consistent way. Experimental results show that ARCTURUS achieves an average precision of 87.8% with 100% block coverage, outperforming compared methods by 38.4%, on average. ARCTURUS takes only 0.15 second to process one function, on average, indicating that it is efficient for practical use.
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