Design of an Iterative Adversarial Resilient Graph Neural Networks for Intrusion Detection in Software Defined Networks & Deployments

Abstract

The increasing adoption of Software Defined Networks (SDNs) in the critical infrastructure has heightened the demand for intelligent and resilient Intrusion Detection Systems (IDSs). Traditional graph-based IDS models can be competent in capturing structural properties of SDN topologies, yet they are all highly susceptible to adversarial perturbations. These perturbations mislead the entire detection pipeline and outrage the security sets of SDN infrastructures in process. Existing GNN-based solutions tend to ignore adversarial topology manipulation as well as the structural defense, which results in very poor generalization performance under attack conditions. In order to address these shortcomings, this paper presents an entirely new GNN-based smart IDS architecture for SDNs that is also adversarially resilient. It consists of five principal analytical modules for the proposed model: (i) Adversarial Edge Perturbation Augmentation (AEPA-GNN), which is meant to augment structure robustness through training over graphs that were intentionally modified through adversaries; (ii) Topology-Guided Mutual Information Bottleneck (TG-MIB), which regularizes node embeddings by minimizing redundant information while reserving features relevant for decisions; (iii) Graph-Adversarial Contrastive Learning for SDN, which imposes consistency of embeddings across clean-adversarial graph views; (iv) Adaptive Defense Graph Transformer (AD-GTNet), which dynamically filters unreliable attention patterns disturbed by adversarial noise; and (v) Robustness-Aware Topological Curriculum Learning (RTC-GNN), which gradually trains the model from clean to heavily perturbed topologies to improve generalization process. The resultant integrated architecture has enhanced adversarial resilience, increasing detection performance under adversarial attacks by up to 17%, whilst also reducing false positives by 9% as compared to state-of-the-art methods. Such a work provides a thorough and robust foundation concerning graph-based IDS deployment in actual SDN settings where adaptive and topology-aware defenses are paramount in process.