Paper Contents
Abstract
In the face of increasingly sophisticated cyber threats, robust intrusion detection systems are essential for safeguarding digital communication networks. This paper explores the application of graph theory in detecting diverse network attacks, including Denial of Service (DoS), Distributed Denial of Service (DDoS), SSH brute force, Man-in-the-Middle (MitM), ARP spoofing, data exfiltration, and DNS tunnelling. By modelling networks as graphswhere nodes representhosts and edges denote traffic flows structural properties such as centrality, connectivity, path anomalies and subgraph patterns are leveraged to identify malicious activity. Graph-based techniques enable scalable and interpretable analysis, with BFS traversal aiding in attack path tracing, weighted edge analysis detecting abnormal traffic volumes, and clustering methods exposing botnet behaviours in DDoS attacks. The proposed approach is particularly effective for large-scale networks, including corporate LANs, ISPs, and university infrastructures, of erring real-time threat detection and proactive defence mechanisms. Integrating graph theory into cybersecurity frameworks enhances threat detection accuracy while supporting the development of resilient, self-healing networks, addressing critical challenges in modern cybersecurity.
Copyright
Copyright © 2025 Sridula O S. This is an open access article distributed under the Creative Commons Attribution License.
