KNOWLEDGE GRAPH-BASED DIGITAL FORENSIC MODEL FOR CRIMINAL NETWORK ANALYSIS

Abstract

With the swift pace of digital technology, the complexity and magnitude of criminal activities have grown, posing serious challenges in contemporary digital forensic investigatory process. Conventional forensic approaches generally look at digital evidence from isolated sources, which makes it hard to uncover hidden relationships, interwoven criminal activities, and intricate networks. This research introduces a novel Knowledge Graph Based Digital Forensic Model for Criminal Network Analysis that combines heterogeneous digital evidence sources and is represented by a structured knowledge graph framework. The proposed model involves a digital evidence collection, entity recognition, relationship extraction, semantic representation and graph-based analytical techniques to find meaningful connections between criminals, devices, locations, communication records, financial activities and other forensic entities. The model can be used for efficient evidence correlation, identification of influential members of the network, detection of hidden associations, and more efficient decision making during criminal investigations, leveraging knowledge graph technologies. The proposed solution mitigates the shortcomings of traditional forensic methods and offers an intelligent, unified and scalable solution to analyze complex criminal networks. The study underscores the transformative role of knowledge graphs and AI-powered analysis in boosting the digital forensic capabilities of law enforcement and cybersecurity practitioners, enabling them to identify, comprehend, and stop complex crime scenarios.