Abstract:
Digital forensics plays a pivotal role in investigating incidents and crimes involving
digital data, leveraging technological footprints left by users in cyberspace. With the exponential
growth in data volumes, traditional forensic techniques often fall short, necessitating the integration
of machine learning (ML) to enhance efficiency and accuracy. This paper explores the applications
of ML in digital forensics, including anomaly detection, malware identification, and user behavior
analysis. Key ML methods such as classification, clustering, and autoencoders are discussed for
their utility in automating evidence analysis, detecting cyber threats, and restoring compromised
data. Despite its advantages, ML faces challenges like data quality requirements and computational
demands. The paper emphareal-time threat detection, and quantum computing integration. Conclusively, machine learning is
identified as a transformative force in modern and future digital forensics, underscoring its criticality
in strengthening cybersecurity frameworks. sizes the evolving role of ML, projecting advancements in automation,
