MATHEMATICAL FILTERING IN CALL HISTORY FORENSICS MODULE

Abstract

Rapid advancements in technology-enabled crime have compelled forensic practitioners to move beyond basic data extraction and focus on in-depth analysis to transform raw mobile artifacts into court-admissible evidence. This study reviews four leading suites—Cellebrite UFED, Oxygen Forensic Detective, Elcomsoft iOS Forensic Toolkit and MOBILedit Pro—assessing their capacity to acquire and parse contacts, messages and, above all, granular call logs from contemporary Android and iOS devices. Each platform delivers dependable extraction pipelines, but recurring deficiencies emerge. Fragmented SQLite tables are incompletely reconstructed, long timelines are displayed with minimal contextual scaffolding, and facilities for correlating activity across multiple handsets are either rudimentary or absent. To bridge these gaps we introduce a lightweight Call History Analysis Module that slots cleanly into existing laboratory workflows. The module normalizes heterogeneous databases into a single, chronologically ordered ledger. It applies mathematical filters to identify frequent call patterns or unusual gaps in communication and generates interactive visualizations, including heat-mapped calendars, duration ridgelines, and ego-network graphs. These tools enable examiners to trace evolving communication patterns quickly while preserving the provenance of every record. An embedded similarity-search engine fingerprints call sequences and compares them across devices, exposing parallel schedules and shared interlocutors that often elude manual inspection or keyword screening. Pilot deployments with live case material confirm the module retrieves additional embedded numbers, mitigates false merges caused by timezone drift, and shortens triage time without compromising chain-of-custody. By complementing rather than replacing commercial toolkits, the proposed module transforms call history from a static list into a rich evidential narrative, advancing the analytical reach of digital forensics at a modest operational cost and helping investigators answer the crucial questions of who, when, how often, and under what context.