A novel adaptive transformer based quantum intrusion detection system for software defined networks

Intrusion detection in Software Defined Networks (SDNs) faces critical challenges due to evolving attack surfaces and increasing traffic complexity. This paper proposes a novel Adaptive Transformer-based Quantum Intrusion Detection System (ATQ-IDS), integrating four core components: Quantum-Inspired Evolutionary Selection (QIES) for optimal feature reduction, a Transformer-Spatial Temporal Network (TSTN) for deep traffic context modeling, Hierarchical Reinforcement Learning-based IDS (HRL-IDS) for adaptive policy control, and a Federated Learning-enabled IDS (FL-IDS) for decentralized, privacy-aware deployment. The QIES component minimizes model overhead by selecting a reduced, high-utility feature set, while the TSTN captures intricate spatial and temporal patterns using attention mechanisms. HRL-IDS ensures decision adaptability in dynamic traffic environments, and FL-IDS supports real-time distributed detection with minimal communication cost. Experimental evaluations on benchmark SDN datasets demonstrate that ATQ-IDS achieves state-of-the-art accuracy of 99.84%, with a false negative rate of 0.12% and inference time below 7 ms. Ablation studies and robustness analyses with confidence intervals confirm the contribution of each module and the model's consistency across runs. This architecture demonstrates high accuracy, adaptability, and real-time applicability, making it a robust solution for modern SDN-based security systems.