Design of an Improved Model for Blockchain Forensic Analysis Using CAKWE, TPDT and HLSM Process

Abstract

The ever-growing dependency on blockchain technology to secure e-evidence in a forensic investigation will involve building architecture that is intrinsically tamper-proof and at the same time optimized contextually for investigative purposes. Most of the extant blockchain forensic frameworks incorporate static evaluation models and a monolithic approach to consensus, rendering them ill-suited to the dynamic forensic context of various event sensitivity, legality requirements, and auditability demands. Existing barriers to practical deployment in high-stakes forensic environments have been created. To begin addressing these gaps, this work presents Forensic-Driven Blockchain Evaluation and Simulation Architecture (ForBESA), which provides a complete simulation-based evaluation framework to com- pare Byzantine Fault Tolerant (BFT), Directed Acyclic Graph (DAG), and Proof-of- Stake (PoS) blockchains against forensic key performance indicators (KPIs). This framework consists of five novel modules. First, the Context-Aware KPI Weighting Engine (CAKWE) develops dynamic generation of KPI weight vectors using forensic incident metadata through the use of a decision tree classifier. Second, the Temporal Provenance DAG Tracker (TPDT) constructs enriched DAGs embedding trans- action timelines and investigator metadata to enhance traces’ future availability. Third, evidence routing across Hyperledger, IOTA, and Ethereum 2.0 will be simulated within the Hybrid Ledger Simulation Module under individual KPIs weighted with forensic considerations. Fourth, the Performance-Forensic Tradeoff Analyzer (PFTA) employs a utilitybased optimization and Pareto analysis to identify architecture suitability based on forensic depth versus performance trade-offs. Finally, the Chain-of-Custody Cryptographic Verifier (C3V) ensures evidence integrity and legal admissibility using smart contracts and zero- knowledge proofs. The improved forensic effectiveness and trace reconstruction up to 98.1% accuracy and 100% tamper detection are scantly recorded in the experiments. This study creates the first model of its kind regarding forensic-aware blockchain evaluation. The system provides precise, legally compliant, and context-responsive digital investigations in process.