A Self-Adaptive Quantum-Capsule Cognitive Security Architecture for Zero-Trust 6G Wireless Networks

Quantum-resilient secure key distribution and deep adaptive intelligence for hybrid IIoT-SDN network defense

Adversarial Attacks on Hybrid Quantum-Classical Interference Classifier in Intelligent O-RAN

Quantum machine learning (QML) is emerging as a key enabler of next-generation artificial intelligence (AI), offering more compact models and enhanced data processing capabilities. However, the integration of QML into AI-enabled network services can introduce new adversarial vulnerabilities, particularly the interface between classical encoders and quantum variational circuits. In this work, we investigate the susceptibility of QML-assisted signal classifiers to adversarial threats in the open radio access network (O-RAN) platforms. We introduce a new family of adversarial attacks, including a novel hybrid quantum-classical poisoning method (QC-Poison), along with hybrid gradient-based attacks (QC-FGSM and QC-PGD). QC-Poison induces long-term misclassification by injecting subtle adversarial, accumulating perturbations in the classical input space that propagate through the quantum encoder, effectively drifting the model’s decision boundary. Evaluation results show that QC-FGSM perturbs inputs based on the hybrid model’s gradients, reducing accuracy from 95.5% to 55.8%, while QC-PGD shows model’s performance reduction to 16.0% by iteratively corrupting quantum circuit parameters via loss maximization. QC-Poison achieves 23.9% accuracy under tight perturbation constraints without accessing training data or internal quantum parameters. The results expose critical blind spots in existing hybrid QML models that can be extended to AI-based features in the O-RAN core services. The study underscores the need for robust quantum-aware defenses that can mitigate stealthy adversarial attacks in distributed and QML-assisted applications in intelligent RAN.

Adversarial Attack Detection in Wireless Networks Using Deep Learning Based Capsule Networks

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.

Quantum-Secured Onion Routing with Federated Learning for 5G-Enabled Spaceborne IoT Networks

In this paper, we recommend an Onion Routing framework powered by federated learning and augmented with E91-based quantum key distribution (QKD) to protect next-generation communication systems like 5G-supported satellite and spaceborne IoT networks. Conventional encryption techniques protect message content but are still susceptible to traffic analysis and developing quantum attacks, necessitating layered, robust protection. In the suggested solution, locally on resourcelimited nodes, lightweight intrusion detection models are trained, whereas just onion-encrypted updates are shared for global aggregation, while keeping privacy intact and bandwidth usage minimum. Onion Routing offers multi-layer anonymity against adversarial eavesdropping, and QKD gives quantum-resilient key distribution immune to cryptanalytic attacks. Experimental testing on the X-IIoTID dataset indicates that the framework records a global accuracy of 98.03% with a loss of 0.0567, which confirms its effectiveness in identifying distributed denial-of-service (DDoS) attacks. Through decentralized intelligence, anonymity, and quantum-level security, this research sets the stage for a scalable and future-proof communication model for vital spaceborne applications.

QuantumGuard: A Post-Quantum Resilient Deception-Driven Framework for Proactive Threat Hunting and Cognitive Honeynet Orchestration in 6G-Enabled Cyber-Physical Systems

The convergence of 6G ultra-reliable low-latency communications, massive cyber-physical actuation, and the looming threat of cryptographically relevant quantum computers exposes a fundamentally new attack surface that is poorly addressed by reactive intrusion-detection paradigms. This study presents QuantumGuard, a proactive cybersecurity framework that inverts the conventional defender posture by integrating cognitive honeynets, post-quantum-secured intelligence channels, and reinforcement-learning-driven deception adaptation across 6G-enabled cyber-physical environments. The principal contribution is the architectural integration of four previously disjoint capabilities—a cognitive honeynet orchestrator, a reinforcement-learning deception policy engine operating under partial observability, a transformer-based MITRE ATT&CK attribution network, and a post-quantum federated intelligence bus se-cured with CRYSTALS-Kyber and CRYSTALS-Dilithium—into a single closed-loop control architecture for 6G cyber-physical systems. To assess feasibility, we report a preliminary evaluation on the CICAPT-IIoT-2024 and Edge-IIoTset benchmark datasets, complemented by a 320-endpoint 6G slice testbed configured, as described in Section IV. Initial results indicate an attacker engagement-retention rate of approximately 96.42 per cent, MITRE ATT&CK technique-attribution accuracy of approximately 91.8 per cent, a 78.6 per cent reduction in median attacker dwell time relative to passive honeypot baselines, and a deception-induced production-traffic overhead of 1.7 per cent. The PQ-FIB sustains a 14.2 ms median post-quantum handshake latency at slice scale. We position these numbers as preliminary evidence of operational viability under the evaluated threat model rather than as fully characterized performance bounds; a follow-up empirical study, planned for a separate publication, will extend the evaluation to deception-aware adversaries and sustained-attack stress conditions.

Beyond encryption: quantum-enhanced behavioral security for the post-quantum era

The framework provides a pragmatic, classifier-agnostic defense layer deployable on freely accessible cloud platforms (Google Colab) without specialized quantum hardware, and offers viable post-quantum hardening for security-critical applications.