Blockchain-Enabled Cyber Threat Intelligence and Social Network Analysis for Homeland Security

Abstract

Introduction: This research investigates the potential of blockchain-enabled novel open-source intelligence (OSINT) to enhance homeland security through advanced social network analysis in cyber threat intelligence (CTI). This approach aims to revolutionize intelligence gathering, analysis, and dissemination by utilizing Distributed Ledger Technology (DLT), consensus mechanisms, link prediction algorithms, clustering algorithms, zero-knowledge proofs, and intrusion detection systems.

Objectives: This The study reviews existing literature and case studies to elucidate the technical foundations, methodologies, and practical applications of blockchain-enabled OSINT in strengthening national security frameworks. Integrating blockchain technology in CTI sharing and social network analysis significantly improves situational awareness, threat detection, and response capabilities while ensuring data privacy and confidentiality.

Methods: The methodology involves implementing Ethereum and Hyperledger Fabric blockchain platforms, using advanced clustering algorithms for social network analysis, and developing smart contracts with Solidity to enforce data-sharing protocols. Support Vector Machine (SVM) and Random Forest algorithm are employed for intrusion detection and threat prediction. Key objectives include developing zero-knowledge proof (ZKP) protocols for privacy preservation, establishing security standards for blockchain-enabled systems, and evaluating the system's performance through controlled experiments.

Results: Results demonstrate a 30% improvement in situational awareness and a 25% increase in threat detection rates.

Conclusions: This research highlights blockchain technology's transformative potential in cybersecurity, contributing to robust protocols and standards for secure data sharing and governance, ultimately enhancing overall cybersecurity frameworks.