Security and Privacy Challenges in Decentralized Ledger Technologies: A Comprehensive Threat Modeling Approach

Abstract

Decentralized Ledger Technologies (DLTs), particularly blockchain systems, have emerged as critical infrastructures for secure, transparent, and tamper-resistant digital transactions across diverse domains. However, they present serious security and privacy issues that go beyond conventional threat environments due to their decentralized and transparent nature. A thorough threat modeling approach for methodically finding, categorizing, and reducing hazards in DLT ecosystems is presented in this study. Attack surface analysis, adversarial capability modeling, STRIDE-based threat classification, and multi-layer security evaluation covering network, consensus, smart contract, data, and application levels are all integrated into the suggested method. A thorough analysis is conducted of the main concerns, which include 51% attacks, Sybil attacks, smart contract vulnerabilities, transaction traceability, metadata leaks, and cross-chain issues. To help with proactive security management and regulatory compliance, an organized risk prioritization methodology is also presented. Comparing the suggested framework to traditional methods, experimental evaluation shows that it greatly increases threat detection coverage, fortifies privacy protection, and boosts system resilience. This study offers a methodical and scalable framework for the development of safe and private decentralized systems.