Implementation of an Integrated Framework for Blockchain Forensic Analysis Combining CAKWE, TPDT, and HLSM Techniques

Abstract

As more and more forensic investigations use blockchain technology to preserve digital data, we will need to build systems that are both contextually optimal for investigations and impossible to break into. Most current blockchain forensic frameworks have a monolithic approach to consensus and static assessment models. This makes them not ideal for the ever-changing forensic context of different event sensitivity, legality, and auditability needs. There are currently barriers to the successful application in high-stakes forensic environments. This work presents the Forensic-Driven Blockchain Evaluation and Simulation Architecture (ForBESA), an extensive simulation-based evaluation framework designed to compare Proof-of-Stake (PoS), Directed Acyclic Graph (DAG), and Byzantine Fault Tolerant (BFT) blockchains against forensic key performance indicators (KPIs) to address existing deficiencies. This framework has five new modules. The Context-Aware KPI Weighting Engine (CAKWE) first makes dynamic KPI weight vector creation by using forensic event metadata and a decision tree classifier. Second, the Temporal Provenance DAG Tracker (TPDT) makes richer DAGs by adding investigator metadata and transaction timings. This makes it easier to find traces in the future. Third, the Hybrid Ledger Simulation Module will simulate how evidence moves between Hyperledger, IOTA, and Ethereum 2.0 using different KPIs that take forensic factors into account. Fourth, the Performance-Forensic Tradeoff Analyzer (PFTA) employs Pareto analysis and utility-based optimization to figure out if a design is good enough by weighing the pros and cons of performance and forensic depth. Finally, the Chain-of-Custody Cryptographic Verifier (C3V) combines smart contracts and zero-knowledge proofs to make sure that the evidence is safe and can be used in court. The experiments hardly show that the forensic efficacy has improved, that trace reconstruction is accurate to 98.1 %, and that tampering is detected 100% of the time. This study presents the inaugural paradigm for forensic-aware blockchain evaluation. The system enables ongoing digital investigations that are precise, legally compliant, and contextually aware.