Developing a Standardized Evaluation Framework for Blockchain-Enabled Digital Forensic Chain of Custody Systems

Abstract

You have received training which included data until the month of October in the year 2023. The increasing amount of digital evidence used in forensic investigations together with the emerging threats from AI deepfake technology and sophisticated cyber attacks has exposed major flaws in existing chain of custody CoC protocols. Traditional systems which depend on centralized databases and manual logs together with paper trails face security risks from both tampering and human mistakes and unauthorized system modifications and disputes over evidence authenticity which typically results in court rejection of evidence. The research paper presents a unified evaluation system that assesses blockchain-based digital forensic CoC systems through multiple assessment dimensions. The assessment examines four vital areas which include technical performance (latency <150 ms and throughput >100 TPS) and security robustness (integrity >99% and AI anomaly detection >95%) and operational efficiency (the system can handle more than 5000 transactions while using less than 80% of its CPU and memory capacity and scoring above 75 on the SUS measurement) and legal compliance (the system meets the requirements of FRE Daubert ISO/IEC 27037 and complete audit processes). The framework establishes quantitative benchmarks through its definition which includes testing protocols (like Hyperledger Caliper and penetration testing) and a composite score range (0 to 100) that enables objective evaluation. The testing of Ethereum (PoA, 10 nodes) and Hyperledger Fabric (PBFT, 6 nodes) prototypes demonstrated latency reductions of 60.7% for Ethereum and 99.6% to 100% integrity rates and 97.2% CNN-LSTM anomaly detection accuracy and throughput rates of 80 to 135 TPS and final scores between 86 and 91. The findings demonstrate a higher capacity to resist tampering while providing evidence tracking and control through smart contract automation which meets the requirements of UN SDGs 9 and 16. The framework connects technical advancements with forensic and legal requirements by providing a system for organizations to measure their operational performance in cybercrime investigation and IoT forensic analysis and multimedia management and deepfake detection systems.