A Unified Analysis of Digital Forensics and Machine Learning in Cybercrime Investigation

Abstract: The rapid increase in cybercrime and the growing volume of digital evidence have made digital forensics an essential component of modern cyber investigations. Digital forensic tools play a crucial role in identifying, acquiring, preserving, analyzing, and presenting digital evidence from computers, mobile devices, cloud platforms, networks, and Internet of Things (IoT) environments. Over the years, numerous commercial and open-source forensic tools have been developed to address a wide range of investigative requirements. Meanwhile, machine learning (ML) techniques have emerged as effective approaches for improving the efficiency of forensic analysis through automated evidence classification, malware detection, anomaly detection, user behavior analysis, and event reconstruction. This paper presents an analysis of digital forensic tools, and their functions, and the application of machine learning techniques to digital evidence analysis throughout the investigation process. The study also examines the application of different ML algorithms across forensic domains based on the characteristics of the available evidence and extracted features. In addition, the study experimentally evaluates selected forensic tools and proposes a generalized architecture for integrating digital forensic tools with machine learning. The comparative analysis highlights how ML can assist in addressing challenges related to data volume, complexity, timeline analysis, evidence correlation, and analysis efficiency. The findings demonstrate that the integration of digital forensic tools with machine learning can enhance the efficiency and scalability of forensic investigations while maintaining the need for evidence validation and expert interpretation.

Keywords: Digital Forensics, Cybercrime Investigation, Digital Forensic Tools, Machine Learning, Digital Evidence, Forensic Analysis, Evidence Classification.