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This systematic literature review surveys research published in major academic databases from January 2010 to December 2021 on machine learning applications in digital forensics. It maps the most studied domains and methods, highlights remaining research gaps, and shows image forensics gains the greatest benefit, with increasing use of CNN-based models. A comprehensive mind map and keyword-driven visual thematic analysis support the findings.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-in-digital-forensics-a-systematic-literature-review/290294/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-in-digital-forensics-a-systematic-literature-review/290294.png","ImageObject",300,407,{"name":92,"@type":93},"Lucas Martin","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-02","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the review address in digital forensics?","Question",{"text":113,"@type":114},"The review targets the challenges caused by the expanding complexity of digital crimes and the growing volume of data, which make evidence handling and investigation more difficult.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What time range and research scope does the systematic literature review cover?",{"text":118,"@type":114},"It reviews research published in major academic databases from January 2010 to December 2021 on applying machine learning in digital forensics.",{"name":120,"@type":111,"acceptedAnswer":121},"Which digital forensics domain and machine learning methods receive the most benefit and attention?",{"text":122,"@type":114},"Image forensics is reported as benefiting the most from machine learning, and CNN-based models are described as the most important and increasingly used methods.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290294,1789641256,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},8796095360427,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","arXiv :2306 .04965v 1 [ cs .CR] 8 Jun 2023  \nMachine Learning in Digital Forensics: A Systematic  \nLiterature Review  \nTahereh Nayerifarda , Haleh Amintoosia , Abbas Ghaemi Bafghia , Ali  \nDehghantanhab  \na Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran b Cyber Science Lab, School of Computer Science, University of Guelph, Guelph, ON,  \nCanada  \nAbstract  \nDevelopment and exploitation of technology have led to the further expansion and complexity of digital crimes. On the other hand, the growing volume of data and, subsequently, evidence is a severe challenge in digital forensics. In recent years, the application of machine learning techniques to identify and analyze evidence has been on the rise in different digital forensics domains. This paper offers a systematic literature review of the research published in major academic databases from January 2010 to December 2021 on the application of machine learning in digital forensics, which was not presented yet to the best of our knowledge as comprehensive as this. The review also identifies the domains of digital forensics and machine learning methods that have received the most attention in the previous papers and finally introduces remaining research gaps. Our findings demonstrate that image forensics has obtained the greatest benefit from using machine learning methods, compared to other forensic domains. Moreover, CNN-based models are the most important machine learning methods that are increasingly being used in digital forensics. We present a comprehensive mind map to provide a proper perspective for valuable analytical results. Furthermore, visual analysis has been conducted based on the keywords of the papers, providing different thematic relevance topics. This research will give digital forensics  \n∗ Haleh Amintoosi  \nEmail addresses: [tahereh.nayerifard@mail.um.ac.ir](tahereh.nayerifard@mail.um.ac.ir) (Tahereh Nayerifard), [amintoosi@um.ac.ir](amintoosi@um.ac.ir) (Haleh Amintoosi), [ghaemib@um.ac.ir](ghaemib@um.ac.ir) (Abbas Ghaemi Bafghi), [adehghan@uoguelph.ca](adehghan@uoguelph.ca) (Ali Dehghantanha)  \nPreprint submitted to Elsevier June 9, 2023  \ninvestigators, machine learning developers, security researchers, and enthusiasts a broad view of the application of machine learning in digital forensics.  \nKeywords: Digital forensic, Machine learning, Convolutional neural networks, Image forensics, Deep learning, SLR  \n1. Introduction  \nWith the expanding use of digital devices and their role in human life and increasing cybercrime, digital forensics (DF) has become a significant area of research. However, there are substantial challenges in digital forensics. One of these challenges is the growing volume of data and its complexity, making the investigation process time-consuming [1 , 2] . In many cases, analysis requires the classification of big data into sets that are not easy to define [3] . Another problem facing digital forensics is the diversity of data. For example, in the Internet of Things (IoT) environments, there are billions of sensors collecting various types of data, posing severe challenges in real-time cybercrime investigation cases [4] . Another essential requirement in digital forensics is accuracy and reliability in the investigation process and its results. Three factors of intelligent computing, speeding up, and reducing time, bring more reliable and accurate results in some cases [5 , 6] .  \nIn recent years, machine learning (ML) techniques in various fields such as image processing, text analysis, voice recognition, optical, and character recognition are still expanding and advancing [7] . In digital forensics, various ML techniques could gather knowledge from large volumes of digital evidence by matching conceptual models to enable data mining and knowledge discovery [1], and help investigators to analyze high volumes of data [8] . These methods are employed to find anomalies and identify patterns in digital forensic inves","cbCainileVXwB2zV","https://ap.wps.com/l/cbCainileVXwB2zV","pdf",6979293,99,"English","# Introduction\n## Prior research","[{\"question\":\"What problem does the review address in digital forensics?\",\"answer\":\"The review targets the challenges caused by the expanding complexity of digital crimes and the growing volume of data, which make evidence handling and investigation more difficult.\"},{\"question\":\"What time range and research scope does the systematic literature review cover?\",\"answer\":\"It reviews research published in major academic databases from January 2010 to December 2021 on applying machine learning in digital forensics.\"},{\"question\":\"Which digital forensics domain and machine learning methods receive the most benefit and attention?\",\"answer\":\"Image forensics is reported as benefiting the most from machine learning, and CNN-based models are described as the most important and increasingly used methods.\"}]","Machine Learning in Digital Forensics - A Systematic Literature Review | PDF",249]