Evaluation Methodologies in Software Protection Research

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-11-02 DOI:10.1145/3702314
Bjorn De Sutter, Sebastian Schrittwieser, Bart Coppens, Patrick Kochberger
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Abstract

Man-at-the-end (MATE) attackers have full control over the system on which the attacked software runs, and try to break the confidentiality or integrity of assets embedded in the software. Both companies and malware authors want to prevent such attacks. This has driven an arms race between attackers and defenders, resulting in a plethora of different protection and analysis methods. However, it remains difficult to measure the strength of protections because MATE attackers can reach their goals in many different ways and a universally accepted evaluation methodology does not exist. This survey systematically reviews the evaluation methodologies of papers on obfuscation, a major class of protections against MATE attacks. For 571 papers, we collected 113 aspects of their evaluation methodologies, ranging from sample set types and sizes, over sample treatment, to performed measurements. We provide detailed insights into how the academic state of the art evaluates both the protections and analyses thereon. In summary, there is a clear need for better evaluation methodologies. We identify nine challenges for software protection evaluations, which represent threats to the validity, reproducibility, and interpretation of research results in the context of MATE attacks and formulate a number of concrete recommendations for improving the evaluations reported in future research papers.
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软件保护研究中的评估方法
终端人(MATE)攻击者可以完全控制被攻击软件运行的系统,并试图破坏软件中嵌入资产的机密性或完整性。公司和恶意软件作者都希望防止此类攻击。这推动了攻击者和防御者之间的军备竞赛,从而产生了大量不同的保护和分析方法。然而,由于 MATE 攻击者可以通过多种不同的方式达到目的,而且不存在普遍接受的评估方法,因此仍然很难衡量保护措施的强度。本调查系统地回顾了有关混淆的论文的评估方法,混淆是抵御 MATE 攻击的一类主要保护措施。在 571 篇论文中,我们收集了 113 个方面的评估方法,从样本集类型和大小、样本处理到执行测量。我们提供了关于学术界如何评估保护和分析的详细见解。总之,显然需要更好的评估方法。我们指出了软件保护评估所面临的九大挑战,这些挑战对 MATE 攻击背景下研究成果的有效性、可重复性和解释性构成了威胁。
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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