Employing Knowledge Graph Technologies for Identifying Potential Network Intrusion Pathways: A Biotechnological Security Framework

Abstract

Current methodologies for analyzing network intrusion paths often fall short in accounting for the dynamic nature of vulnerabilities and typically overlook the potential failure scenarios in vulnerability exploitation. Furthermore, the complexity of security threat intelligence, which originates from diverse and intricate sources, presents significant challenges in terms of comprehension and dissemination. To address these issues, this research introduces a sophisticated knowledge graph-based approach designed to identify potential pathways for network intrusion. This study pioneers the construction of a knowledge graph (KG) for cyberspace security risks by methodically mapping original intrusion intelligence features from a high-dimensional space to a more manageable low-dimensional space. This structured representation allows for a more nuanced understanding and tracking of security threats, leveraging the context-sensitive capabilities of event stream processing to predict and detect potential network intrusions accurately. To validate the effectiveness of this novel approach, experiments were conducted comparing the knowledge graph-based method with traditional intrusion detection systems. The results demonstrate not only the feasibility of generating a comprehensive KG of potential network intrusion paths but also the superior efficacy of this KG-based approach in identifying potential intrusion paths. By integrating biotechnological methodologies, this research enhances the predictive capabilities of network security systems, offering a robust framework for the proactive management of cyber threats.