Federated Secure Intelligent Intrusion Detection and Mitigation Framework for SD-IoT Networks using ViT-GraphSAGE and Automated Attack Reporting

Software-Defined Internet of Things (SD-IoT) networks enabled intelligent network management through their dynamic features but expose centralized infrastructure to complex cyberattacks that put the system in great danger. In order to address this, a novel Federated Secure Intelligent Intrusion Detection and Mitigation framework with Automated Attack Reporting for SD-IoT network is proposed. The combination of Vision Transformer (ViT) and GraphSAGE architecture enables the model to process network relationships globally and locally which effectively increases intrusion detection performance. Besides, in real-time, to dynamically reroute network traffic and isolate the compromised nodes, a Multi-Agent Deep Q-Learning (MA-DQL) based mitigation strategy is employed which minimizes attack impact. For enabling collaborative and secure communication without centralized data exposure, the edge nodes are integrated with Federated Learning (FL) that ensures privacy-preserving and distributed model training. The proposed system also incorporates a Flan-T5 Transformer-based Automated Attack Reporting System which develops comprehensive forensic reports that expose security threats and their corrective actions. Through this proposed framework, accurate threat detection of 98.06% with real-time adaptative operation and efficient attack containment is accomplished in parallel with reduced computational load which ensures secure operation of SD-IoT systems.