DIFFUSION GRAPH TRANSFORMER (DIGTRAN): A NOVEL SELF SUPERVISED DIFFUSION AND KNOWLEDGE GRAPH ENHANCED FRAMEWORK FOR PROACTIVE NETWORK CYBERSECURITY

Abstract

Modern networks face an explosion of malicious programs and sophisticated attacks that outpace the capabilities of traditional signature‑based intrusion detection systems (IDSs). Recent progress in deep learning has improved anomaly detection and automated responses, yet most IDSs rely on supervised learning and lack the ability to synthesize scarce attack patterns or encode domain knowledge. Meanwhile, diffusion models have emerged as powerful generative models that can generate realistic high‑dimensional data and denoise corrupted inputs[22]. In cybersecurity, diffusion models remain largely unexplored but offer opportunities to enhance detection and robustness[23]. This paper proposes Diffusion Graph Transformer (DiGTran), a novel framework that integrates self‑supervised diffusion pretraining, graph transformers augmented with a knowledge graph and contrastive learning, and a reinforcement learning response agent. DiGTran leverages diffusion models to denoise network flow representations and synthesize rare attack patterns, uses a knowledge graph to inject domain expertise into a graph‑based transformer encoder, and employs contrastive and diffusion pretraining to enable zero‑shot adaptation. We survey related work and present a results table comparing state‑of‑the‑art IDSs. We also provide mathematical formulations of diffusion processes, contrastive loss and the reinforcement learning update. While full implementation and evaluation are left for future work, we discuss design considerations and hypothesize that DiGTran can improve detection accuracy, robustness and adaptability compared to existing models.