Scholarlink

<p>Integrity, traceability, and operational continuity issues plague blockchain implementations. This study’s security framework includes a multilayer forensic architecture, dynamic trust-based consensus mechanism, decentralised key-recovery protocol, and real-time anomaly detection module. Continuous transactional and cryptographic metadata records at the forensic layer allow event sequence reconstruction during and after disruptive episodes. When abnormalities are found, the consensus mechanism switches between standard validation and trust-weighted participation to quickly contain rogue nodes. Distributed reconstruction improves key-management by eliminating single-point recovery. To improve responsiveness, the detection module finds aberrant patterns in live transaction streams using behavioural analysis and rule-driven categorisation. Blockchain lifespan, traceability, and quick recovery after security breaches are ensured by these components. AFL provides a multi-layer forensic framework capturing transactional, cryptographic, and temporal data for 99% accurate source tracing post-attack, while the current DTHC dynamically alternates between traditional consensus and reputation-based consensus during an attack, cutting downtime by 40% and isolating malicious nodes by 95%. RSRP uses Shamir’s Secret Sharing redundancy to recover cryptographically secure keys when 40% of nodes are compromised. AADM delivers real-time notifications with 95% accuracy and 2% false alarms using machine-learning-based anomaly detection. Rapid recovery, accurate source tracing, and safe key reconstruction would strengthen blockchain. This complete strategy minimises disturbance to operations, increases trust in blockchain ecosystems, and enables the development of resilient, attack-tolerant decentralised system architectures.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhancing blockchain security and source traceability using adaptive forensic layer and dynamic trust hybrid consensus

  • 期刊论文

  • 发表日期：

    2025年12月27日

查看全文
  • Hemlata R. Kosare,
  • Amol Zade

摘要

Integrity, traceability, and operational continuity issues plague blockchain implementations. This study’s security framework includes a multilayer forensic architecture, dynamic trust-based consensus mechanism, decentralised key-recovery protocol, and real-time anomaly detection module. Continuous transactional and cryptographic metadata records at the forensic layer allow event sequence reconstruction during and after disruptive episodes. When abnormalities are found, the consensus mechanism switches between standard validation and trust-weighted participation to quickly contain rogue nodes. Distributed reconstruction improves key-management by eliminating single-point recovery. To improve responsiveness, the detection module finds aberrant patterns in live transaction streams using behavioural analysis and rule-driven categorisation. Blockchain lifespan, traceability, and quick recovery after security breaches are ensured by these components. AFL provides a multi-layer forensic framework capturing transactional, cryptographic, and temporal data for 99% accurate source tracing post-attack, while the current DTHC dynamically alternates between traditional consensus and reputation-based consensus during an attack, cutting downtime by 40% and isolating malicious nodes by 95%. RSRP uses Shamir’s Secret Sharing redundancy to recover cryptographically secure keys when 40% of nodes are compromised. AADM delivers real-time notifications with 95% accuracy and 2% false alarms using machine-learning-based anomaly detection. Rapid recovery, accurate source tracing, and safe key reconstruction would strengthen blockchain. This complete strategy minimises disturbance to operations, increases trust in blockchain ecosystems, and enables the development of resilient, attack-tolerant decentralised system architectures.