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dc.contributor.authorDeen, MTA
dc.contributor.authorHettige, B
dc.date.accessioned2023-06-28T05:35:16Z
dc.date.available2023-06-28T05:35:16Z
dc.date.issued2022
dc.identifier.urihttp://ir.kdu.ac.lk/handle/345/6447
dc.description.abstractAs a branch of forensic science, Digital Forensics is concerned with identifying, acquiring, processing, analysing, and reporting on digital data. For law enforcement investigators, Digital Forensics support is crucial since electronic evidence is present in almost all criminal activities. An array of electronic evidence can be gathered from a variety of sources, including computers, smartphones, remote storage, unmanned aerial systems, and shipborne equipment. The main objective in Digital Forensic is to extract data from electronic evidence, process it into actionable intelligence and present the findings for prosecution. The success, efficiency, and efficacy of a typical forensic inquiry are significantly influenced by the knowledge and prior experience of the investigator or any security agent. The outcomes of a digital investigation will be more effective and efficient if the power of intelligence in the available computer resources is utilized. In modern computer science, Artificial Intelligence (AI) is a well-established field that can often provide a means of solving computationally complex or large problems in a realistic timeframe. The influence of AI on several fields in modern society and its achievements throughout time suggest that it can help with a variety of challenging Digital Forensics investigative issues. This review paper outlines various methods of evaluating, optimizing and standardizing applications of artificial intelligence and Automation models used in digital forensics.en_US
dc.language.isoenen_US
dc.subjectDigital Forensicen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectAutomationen_US
dc.subjectMachine Learningen_US
dc.subjectIntelligent Forensicsen_US
dc.titleA Review on the Application of Artificial Intelligence and Automation in Digital Forensicsen_US
dc.typeArticle Full Texten_US
dc.identifier.facultyComputingen_US
dc.identifier.journalKDU IRCen_US
dc.identifier.pgnos321-326en_US


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