Neuromorphic quantum adversarial learning (NQAL): a bio-inspired paradigm for DNS over HTTPS threat detection
Year: 2025
Type: article
Abstract: Exponentially expanding domain name system (DNS) over HTTPS (DoH) has significantly increased privacy but has also quietly masked malicious activities, rendering traditional threat detection systems meaningless. Existing deep learning-powered systems are unable to detect fleeting micro-temporal abnormalities in encrypted streams, are too costly for real-time operation, and are still vulnerable to adversarial attacks. To overcome these complex issues, this work proposes a new architecture—Neuromorphic Quantum Adversarial Learning (NQAL)—a bio-inspired, zero-knowledge-supported detection mechanism combining spiking neural networks (SNNs), quantum noise injection (QNI), and federated swarm intelligence to immunize, rather than detect, DoH-based attacks. The method relies on a neuromorphic model employing Dynamic Spiking Graph Attention (DSGAT) and Spike-Timing-Dependent Plasticity (STDP) to encode encrypted traffic as dynamic spike trains to enable ultra-fast, energy-efficient inference on processors such as Intel Loihi and BrainChip Akida. Quantum adversarial noise, emulated through stochastic perturbations created from quantum random walks, is injected during training to build gradient-obfuscating robustness. A threat immunization engine powered by adversarial GANs and quantum perturbations to simulate zero-day anomalies for pre-conditioning the model. Zero-knowledge verification is guaranteed through zk-SNARKs for privacy-preserving confirmation of anomalies without decrypting packets. Empirical studies confirm that NQAL achieves 99.18% accuracy, ¡1ms latency, and 10x less energy consumption than GPU-based models, while also being robust to both classical and quantum adversarial attacks. Feasibility, novelty, and decentralization of the system amount to a paradigm shift from existing architectures—hence, making NQAL a resilient frontier in encrypted traffic immunization.
Authors: Basharat Ali, Guihai Chen
Institution: Nanjing University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,040
OpenAlex ID: W4417451629