The vulnerability of wireless networks to misclassification and security oversight is increased by adversarial attacks using Deep Learning (DL)-based Intrusion Detection Systems (IDS). Because they are unable to distinguish small fluctuations in signal data and retain spatial hierarchy, traditional neural networks prefer Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which are highly prone to adversarial perturbations. In this research, a Capsule Network (CapsNet)-based framework detecting adversarial attacks in wireless networks is proposed. The network uses RF-aware capsule representations and dynamic routing to improve hierarchical feature learning and robustness against adversarial perturbations. To successfully search for adversarial distortion, the proposed model extracts significant network traffic features, encodes spatial hierarchy via primary and digit capsules, and uses a reconstruction loss function. To improve the model's robustness against sophisticated attack tactics, an exploratory quantum-assisted CapsNet implementation for preliminary investigation is also included. Experimental evaluation on benchmark wireless intrusion datasets shows that CapsNets outperform conventional CNNs and Long Short-Term Memory (LSTM) models, achieving 98.3% accuracy under normal conditions and maintaining 91.3% precision even under adversarial attack, compared to 87.5% and 72.4% for CNNs and 90.2% and 78.6% for LSTMs, respectively. In addition, compared with traditional DL models, CapsNets show a 34% improvement in robustness metrics. The proposed CapsNet system achieved 98.3% detection accuracy and a reduced False Positive Rate across several adversarial attack scenarios.
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