Big Data Analytics Framework for Cloud-Based Digital Forensic Investigations

The rapid expansion of cloud computing infrastructures has introduced significant challenges in conducting reliable digital forensic investigations due to the distributed and dynamic nature of cloud-hosted data. This study proposes a scalable big data analytics framework designed to enhance the efficiency of cloud-based forensic investigations through automated evidence acquisition, semantic data harmonization, and intelligent anomaly detection. The proposed framework was evaluated in a simulated multi-cloud environment using real-time telemetry streams and heterogeneous forensic datasets. Experimental results demonstrate a 36.8% reduction in evidence acquisition latency and a harmonization accuracy of 93.2% across distributed data sources. Furthermore, the anomaly detection module achieved a detection accuracy of 93.0% with an F1-score of 91.6%, outperforming baseline forensic models by an average margin of 7.5%. The framework also demonstrated a timeline reconstruction efficiency of 91.7%, enabling accurate correlation of temporally dispersed cyber-attack events.

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