Paper Title
AI-POWERED DIGITAL FORENSICS FRAMEWORK FOR INTELLIGENT CYBERCRIME INVESTIGATION

Abstract
The exponential growth of cybercrime and digital data has significantly increased the complexity of digital forensic investigations. Traditional forensic methods rely heavily on manual analysis, making them time-consuming, error-prone, and inefficient when handling large-scale heterogeneous datasets. This paper proposes an AI-powered digital forensics framework that integrates machine learning, deep learning, soft computing, and explainable artificial intelligence (XAI) techniques to automate and enhance forensic investigations. The framework introduces a layered architecture consisting of data acquisition, evidence processing, AI-based analysis, explainability, and reporting modules. The proposed system improves evidence correlation, accelerates malware detection, enhances anomaly identification, and ensures transparency for legal admissibility. Experimental evaluation demonstrates improved accuracy, reduced investigation time, and better scalability compared to traditional forensic approaches. The proposed framework contributes to modernizing digital forensic practices in cloud, IoT, and big data environments. Keywords - Digital Forensics, Artificial Intelligence, Machine Learning, Deep Learning, Explainable AI, Cyber Security, Soft Computing.