A Capsule Network (CapsNet)-based framework detecting adversarial attacks in wireless networks is proposed, which uses RF-aware capsule representations and dynamic routing to improve hierarchical feature learning and robustness against adversarial perturbations.
Sneha George, P. R, K. K· Journal of Trends in Compute...· 0 citations
A Self-Adaptive Quantum-Capsule Cognitive Security Architecture (SA-QCCSA) is introduced for zero-trust adversarial defense in 6G wireless networks, integrating Quantum-optimized Capsule Networks (Q-CapsNet) with cognitive threat orchestration for real-time cyber-attack mitigation. Multidimensional 6G network traffic is modeled as temporal–spectral feature tensors and processed using a lightweight CNN encoder followed by primary and higher-order capsules that preserve hierarchical attack patterns. A quantum-enhanced dynamic routing mechanism, implemented using a Variational Quantum Optimization layer, adaptively tunes capsule coupling coefficients to minimize adversarial uncertainty and maximize class separability under strong evasion attacks. The framework is trained using a hybrid adversarial learning strategy that combines margin-based capsule loss with contrastive regularization, enabling robustness against FGSM, BIM, PGD, and CW attacks. Experiments conducted on CIC-IDS2017, NSL-KDD, and AWID Wi-Fi intrusion datasets demonstrate that SA-QCCSA achieves 98.7% detection accuracy, 0.986 F1-score, and 0.993 AUC, significantly outperforming conventional CNN (94.1% accuracy) and LSTM (91.6% accuracy) models. Under high-strength PGD attacks, the proposed model maintains 94.8% accuracy, while CNN performance degrades below 78%, confirming superior adversarial resilience. A cognitive zero-trust control plane dynamically adjusts quantum routing depth based on real-time threat entropy, enabling self-learning, self-healing, and proactive attack containment for future 6G and beyond wireless networks.
Sneha George, R. Joy, K. Karuppasamy· 2026 International Conferenc...· 0 citations