Identifying Authorship in Malicious Binaries: Features, Challenges & Datasets

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-03-26 DOI:10.1145/3653973
Jason Gray, Daniele Sgandurra, Lorenzo Cavallaro, Jorge Blasco
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Abstract

Attributing a piece of malware to its creator typically requires threat intelligence. Binary attribution increases the level of difficulty as it mostly relies upon the ability to disassemble binaries to obtain authorship-related features. We perform a systematic analysis of works in the area of malware authorship attribution. We identify key findings, some shortcomings of current approaches and explore the open research challenges. To mitigate the lack of ground truth datasets in this domain, we publish alongside this survey the largest and most diverse meta-information dataset of 17,513 malware labeled to 275 threat actor groups.

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识别恶意二进制文件中的作者身份:特征、挑战和数据集
将恶意软件归属于其创建者通常需要威胁情报。二进制归属增加了难度,因为它主要依赖于反汇编二进制文件以获取作者相关特征的能力。我们对恶意软件作者归属领域的工作进行了系统分析。我们确定了主要发现、当前方法的一些不足之处,并探讨了有待解决的研究难题。为了缓解该领域缺乏基本真实数据集的问题,我们在发布本调查报告的同时,还发布了最大、最多样化的元信息数据集,该数据集包含 17,513 个恶意软件,标记为 275 个威胁行为者团体。
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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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