APPLICATION OF MACHINE LEARNING AND AUTOMATED PROCESSES IN DIGITAL FORENSICS

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,