Next-Generation Cyber Defense Powered by Graph-Based Threat Intelligence and Automated Attack Path Analysis

Abstract

The increasing complexity of modern cyber threats, distributed enterprise infrastructures, and interconnected digital environments has created significant challenges for conventional cybersecurity defense mechanisms. Traditional security solutions often operate independently, making it difficult to identify relationships between vulnerabilities, malicious activities, compromised assets, and potential attack routes. This research proposes a next-generation cyber defense framework powered by graph-based threat intelligence and automated attack path analysis to improve enterprise cybersecurity capabilities. The proposed framework integrates security event monitoring, threat intelligence enrichment, graph-based knowledge representation, vulnerability correlation, and automated attack path identification. Graph structures are utilized to represent relationships among users, devices, applications, network resources, vulnerabilities, and threat indicators, enabling security analysts to identify complex attack patterns across distributed environments. Automated graph traversal and risk assessment mechanisms prioritize critical attack paths based on vulnerability severity, asset importance, threat activity, and potential business impact. The framework further incorporates machine learning techniques to improve threat classification and predictive attack detection. By combining contextual threat intelligence with automated security analysis, the proposed approach aims to strengthen proactive threat identification, accelerate incident response, and improve organizational cyber resilience. The research establishes a scalable and intelligent foundation for next-generation enterprise security operations.