A Systematic Literature Review of Secure Instant Messaging Applications from a Digital Forensics Perspective

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Abdur Rahman Onik, Joseph Brown, Clinton Walker, Ibrahim Baggili
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引用次数: 0

Abstract

The use of instant messengers in organized crime has made retrieving digital evidence from these platforms crucial in investigations. However, the wide implementation of end-to-end encryption (e2ee) has made forensic analysis challenging since all data and multimedia stored in these platforms are encrypted. To retrieve evidence, forensic examiners rely on artifacts produced by secure messaging applications. Therefore, identifying the artifacts that can be gathered from secure messaging applications is crucial during emergency analysis, as law enforcement agencies monitor these platforms. This literature review aims to provide a comprehensive understanding of artifact retrieval and forensic analysis trends by summarizing studies conducted since inception on popular secure messaging applications, including WhatsApp, Signal, Telegram, Wickr, and Threema. The review covers artifact retrieval, forensic methodologies, and tools. The review findings are significant to the cybersecurity and research communities, as well as the users of these applications. Forensic investigators can leverage the information during their investigations, cybersecurity practitioners can identify weaknesses in implementation to develop better security policies, and users can make informed decisions regarding their privacy.
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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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