[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127630-en":3,"doc-seo-127630-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127630,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Machine-Learning Forensics: State of the Art in the Use of Machine-Learning Techniques for Digital Forensic Investigations within Smart Environments","Smart environments and automation have rapidly accelerated adoption across domains, bringing new obstacles to conventional digital forensic investigation (DFI). Smart environments rely on diverse IoT devices whose data are heterogeneous, distributed, and massive, overwhelming investigators and slowing or disabling standard DFI workflows. With digital crimes increasing, more advanced and reliable DFI procedures are required. This paper reviews recent research integrating machine learning into digital forensics, evaluates applications within smart environments, and outlines expected future directions to reduce manual effort.","applied sciences  \nReview  \nMachine-Learning Forensics: State of the Art in the Use of Machine-Learning Techniques for Digital Forensic Investigations within Smart Environments  \nLaila Tageldin 1, * and Hein Venter 2  \nCitation: Tageldin, L.; Venter, H.  \nMachine-Learning Forensics: State of the Art in the Use of  \nMachine-Learning Techniques for Digital Forensic Investigations within Smart Environments. Appl. Sci. 2023, 13, 10169. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)app131810169  \nAcademic Editors: Dimitris Mourtzisand Gino Iannace  \nReceived: 21 June 2023  \nRevised: 16 August 2023  \nAccepted: 7 September 2023  \nPublished: 10 September 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Computer Science, Sudan University of Science and Technology, Khartoum 11111, Sudan  \n2 Department of Computer Science, University of Pretoria, Pretoria 0002, South Africa; [hventer@cs.up.ac.za](hventer@cs.up.ac.za)  \n* Correspondence: [laylataj@hotmail.co.uk](laylataj@hotmail.co.uk)  \nAbstract: Recently, a world-wide trend has been observed that there is widespread adoption across all fields to embrace smart environments and automation. Smart environments include a wide variety of Internet-of-Things (IoT) devices, so many challenges face conventional digital forensic investigation (DFI) in such environments. These challenges include data heterogeneity, data distribution, and massive amounts of data, which exceed digital forensic (DF) investigators' human capabilities to deal with all of these challenges within a short period of time. Furthermore, they significantlyslow down or even incapacitate the conventional DFI process. With the increasing frequency of digital crimes, better and more sophisticated DFI procedures are desperately needed, particularly in such environments. Since machine-learning (ML) techniques might be a viable option in smart environments, this paper presents the integration of ML into DF, through reviewing the most recent papers concerned with the applications of ML in DF, specifically within smart environments. It also explores the potential further use of ML techniques in DF in smart environments to reduce the hard work of human beings, as well what to expect from future ML applications to the conventional DFI process.  \nKeywords: IoT devices; smart environments; digital forensics; machine-learning techniques  \n1. Introduction  \nCurrently, smart environments offer various technologies and services, such as smart transport systems, smart vehicles, smart homes, smart urban lighting, integrated travel ticketing, smart energy grids, and smart sensors [1] . These technologies strongly depend on the use of small electronic chips and electromechanical devices (i.e., IoT devices), such as sensors, wireless technologies, radio-frequency identification (RFID) devices, localisation technologies, and near-field communication devices [1] .  \nThe wide variety of IoT devices used within smart environments makes it very difficult to perform digital forensics (DF) in this environment. The challenge for DF professionalsand practitioners is that standard industrial DF equipment and its capabilities concerning conventional computing operating systems are not coping with the smart environment due to its complex, heterogeneous, and distributed nature [2] .  \nThe problem raised in this paper is that little to no reliable DF applications or DF directives currently exist to retrieve data from Internet-of-Things (IoT) devices in the event of a digital attack, an active investigation, or a litigation request within a smart environment [3] . Thus, researchers and practitioners in the ","cbCaimb7JKNjqtPJ","https://ap.wps.com/l/cbCaimb7JKNjqtPJ","pdf",1016781,2,1,12,"English","en",105,"# Introduction\n## Challenges of digital forensics in smart environments\n## Need for machine-learning assisted digital forensics\n# Machine-learning integration and review scope\n## Recent ML applications in digital forensics\n## Future directions for ML in DFI","[{\"question\":\"Why are conventional digital forensics workflows difficult in smart environments?\",\"answer\":\"Smart environments generate heterogeneous, distributed, and massive data from many IoT devices, which exceeds investigators’ human capacity and can slow down or incapacitate conventional DFI processes.\"},{\"question\":\"What problem does the paper highlight about digital forensic investigations for IoT devices?\",\"answer\":\"Little to no reliable DF applications or directives exist to retrieve data from IoT devices during attacks, active investigations, or litigation requests in smart environments.\"},{\"question\":\"How does the paper position machine learning for digital forensics?\",\"answer\":\"Machine-learning techniques are presented as a viable option in smart environments, with the paper reviewing recent work on ML applications in digital forensics and discussing potential future use to improve efficiency and evidence discovery.\"}]","Machine-Learning Forensics: State of the Art in the Use of Machine-Learning Techniques for Digital Forensic Investigations within Smart Environments | 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are conventional digital forensics workflows difficult in smart environments?","Question",{"text":76,"@type":77},"Smart environments generate heterogeneous, distributed, and massive data from many IoT devices, which exceeds investigators’ human capacity and can slow down or incapacitate conventional DFI processes.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What problem does the paper highlight about digital forensic investigations for IoT devices?",{"text":81,"@type":77},"Little to no reliable DF applications or directives exist to retrieve data from IoT devices during attacks, active investigations, or litigation requests in smart environments.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the paper position machine learning for digital forensics?",{"text":85,"@type":77},"Machine-learning techniques are presented as a viable option in smart environments, with the paper reviewing recent work on ML applications in digital forensics and discussing potential future use 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