Date of Publication : March 2026

Abstract: The discipline of digital forensics stands at a critical juncture, besieged by the exponential growth of digital evidence and the increasing sophistication of anti-forensic techniques. Contemporary investigations involving the Internet of Things (IoT), Industrial Control Systems (ICS), and automotive data are buckling under the weight of manual acquisition and centralized logging. This paper proposes a novel, comprehensive framework: Blockchain-Enhanced Digital Forensics (BEDF). We synthesize the immutable trust of Distributed Ledger Technology (DLT) with the high-throughput analytical capabilities of Artificial Intelligence (AI) and the semantic structure of Data- Information- Knowledge-Wisdom (DIKW) graphs. Unlike existing solutions that focus primarily on cryptocurrency tracking, BEDF addresses the internal Chain of Custody (CoC) for enterprise environments. We provide a rigorous formal threat model utilizing the Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, Elevation of privilege (STRIDE) methodology to validate system resilience, detail specific Convolutional Neural Networks (CNN) / Long Short-Term Memory (LSTM) architectures for automated malware detection to ensure reproducibility, and offer a theoretical complexity analysis demonstrating the viability of Practical Byzantine Fault Tolerance (PBFT) consensus for high-velocity forensic logging.

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