Anshunkang Zhou, Yikun Hu, Xiangzhe Xu, Charles Zhang
{"title":"ARCTURUS: Full Coverage Binary Similarity Analysis with Reachability-Guided Emulation","authors":"Anshunkang Zhou, Yikun Hu, Xiangzhe Xu, Charles Zhang","doi":"10.1145/3640337","DOIUrl":null,"url":null,"abstract":"<p>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. </p><p>In this paper, we propose <span>ARCTURUS</span>, 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 <span>ARCTURUS</span> achieves an average precision of 87.8% with 100% block coverage, outperforming compared methods by 38.4% on average. <span>ARCTURUS</span> takes only 0.15 seconds to process one function on average, indicating that it is efficient for practical use.</p>","PeriodicalId":50933,"journal":{"name":"ACM Transactions on Software Engineering and Methodology","volume":"20 1","pages":""},"PeriodicalIF":6.6000,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Software Engineering and Methodology","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3640337","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
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 paper, 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 seconds to process one function on average, indicating that it is efficient for practical use.
期刊介绍:
Designing and building a large, complex software system is a tremendous challenge. ACM Transactions on Software Engineering and Methodology (TOSEM) publishes papers on all aspects of that challenge: specification, design, development and maintenance. It covers tools and methodologies, languages, data structures, and algorithms. TOSEM also reports on successful efforts, noting practical lessons that can be scaled and transferred to other projects, and often looks at applications of innovative technologies. The tone is scholarly but readable; the content is worthy of study; the presentation is effective.