A Deterministic Forensic Preprocessing Framework for Heterogeneous Network Datasets: Formal Foundations, Implementation, and Empirical Validation
It addresses the reproducibility gap in digital forensic investigations by providing a formal, deterministic preprocessing method for heterogeneous network evidence.
The paper introduces a deterministic forensic preprocessing framework that converts heterogeneous network datasets into a reproducible canonical form, achieving 100% output consistency across repeated runs and scalable performance on datasets up to hundreds of millions of records.
Digital forensic investigations increasingly depend on preprocessing heterogeneous network evidence from intrusion detection systems, IoT devices, and enterprise traffic logs. Incompatible schemas and timestamp formats hinder evidence correlation and timeline reconstruction, while current ad hoc approaches offer no mechanism to verify consistency across runs or analysis, creating reproducibility gaps that challenge evidence admissibility. This paper introduces a deterministic forensic preprocessing framework that converts heterogeneous network datasets into a reproducible canonical form. The framework formalises three preprocessing transformations: schema normalisation, temporal normalisation, and provenance tracking. These transformations are specified using set-theoretic definitions and supported by four theorems establishing determinism, information preservation, and provenance completeness. A chunk-based architecture provides O(c) bounded memory. Empirical evaluation across UNSW-NB15, IoT-23, and TON_IoT demonstrates 100% output consistency across repeated runs, robust temporal normalisation completeness over heterogeneous timestamp formats, and scalable performance from millions to hundreds of millions of records.