Automating Digital Forensic Investigations: The Impact of Machine Learning on Electronic Evidence

Automating Digital Forensic Investigations: The Impact of Machine Learning on Electronic Evidence

연구 분야: Analysis

학회: 2025 17th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

Digital forensic investigations have become more complicated along with larger scales because of a dramatic expansion in digital data. Conventional data examination techniques depend on hand-based procedures that remain slow and need significant resources along with being prone to mistakes from human operators. Mobile technology has disrupted electronic evidence analysis by introducing machine learning (ML) which fundamentally upgrades the operational efficiency and measurement accuracy and data handling capability beyond traditional methods. Digital forensic automation can be achieved through ML algorithms when handling tasks which include identifying data sources and detecting anomalies and recovering evidence and analyzing networks and multimedia content. The implementation of ML as a digital forensics solution comes with multiple barriers that affect the results including inadequate data quality and biased algorithms and the need for explainability and the risk of adversarial attacks and legal and ethical restrictions. The research investigates machine learning effects on digital forensic investigations by discussing modern implementation together with obstacles faced in addition to expected research pathways. The proper resolution of these challenges remains vital to establish reliable and fair and lawful ML-based forensic instruments for criminal along with civil examination activities.