Big data and machine learning in digital forensics: Predictive technology for proactive crime prevention

Abstract

The integration of big data analytics and machine learning [ML] has transformed digital forensics, enabling predictive technologies that proactively prevent cybercrimes and enhance crime investigation processes. As cyber threats grow in scale and complexity, traditional forensic methods face limitations in scalability, efficiency, and accuracy. Big data platforms, such as Hadoop and Spark, combined with advanced ML techniques, offer revolutionary approaches to address these challenges. By analysing vast datasets—including network traces, file metadata, and logs—big data enables the identification of hidden patterns and trends in criminal activities. Simultaneously, ML models, such as supervised and unsupervised learning algorithms, facilitate anomaly detection, predictive risk assessment, and real-time analysis. This article explores how the synergy of big data and ML advances digital forensics by automating large-scale forensic processes, identifying potential cyber threats, and predicting high-risk scenarios. It also discusses the integration of predictive technologies into forensic frameworks, examining the role of automation and real-time analytics in strengthening investigations. The challenges of implementing big data and ML, including issues of data quality, ethical concerns, and legal compliance, are critically analysed. Finally, the article highlights future trends, such as the role of quantum computing and explainable AI, in shaping the future of digital forensics. By providing a comprehensive overview of the field, this study emphasizes the transformative potential of big data and ML in proactive crime prevention, offering insights for researchers, regulators, and industry stakeholders.