Deep Learning (DL) Oriented Forensic Analysis

Abstract

Cyber-attacks are now more prevalent than ever before in all aspects of our daily lives. As a result of this circumstance, both individuals and organizations are fighting cybercrime on a regular basis. Furthermore, today's hackers have advanced a step further and are capable of employing complex cyber-attack strategies, exacerbating the problem. Some of these approaches are minute and undetectable, and they frequently masquerade as genuine requests and directives. To combat this threat, cyber security professionals, as well as digital forensic investigators, are constantly compelled to filter through massive and complicated pools of data, also known as Big Data, in order to uncover Potential Digital Forensic Evidence. that can be used as evidence in court. Potential Digital Evidence can then be used to assist investigators in reaching certain conclusions and/or judgments. The fact that Big Data frequently comes from various sources and has diverse file formats makes cyber forensics even more difficult for investigators. When it comes to the processing of vast amounts of complicated data for forensic purposes, forensic investigators typically have less time and budget to fulfil the rising demands. This paper will be studying how to incorporate Deep Learning cognitive computing approaches into Cyber Forensics Keywords: Deep Learning, Forensic Analysis, Artificial Intelligence, Online Safety, Evidence BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security & Forensics. Open Access. Distributed Free Citation: Herbert Cyril Dodoo (2022): Deep Learning (DL) Oriented Forensic Analysis Book Chapter Series on Research Nexus in IT, Law, Cyber Security & Forensics. Pp 320-328 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P51