FORENSIC INTELLIGENCE: A HYBRID FRAMEWORK FOR AUTOMATED ARTIFACT COLLECTION AND ANALYSIS

Abstract

 The increasing sophistication of cyber threats necessitates advanced digital forensic approaches for effective detection and investigation. This research presents a comprehensive framework that integrates memory forensics, digital artifact analysis, and automated evidence collection to enhance cybercrime investigations. By simulating real-world attack scenarios—including file manipulation, removable media interactions, and malicious downloads—diverse forensic artifacts were systematically extracted and analyzed. The proposed framework ensures evidence integrity, legal admissibility, and structured presentation through both live and dead forensic techniques. Python-based automation was employed to streamline IP tracing, social media profiling, and URL exploration, reducing manual effort and improving efficiency. Memory forensics was crucial in identifying volatile data such as rogue processes and network connections, supporting the reconstruction of malicious activities. Overall, this study advances digital forensics by introducing an automated, legally compliant, and comprehensive methodology for identifying and mitigating modern cyber threats.