Neuromorphic quantum adversarial learning (NQAL): a bio-inspired paradigm for DNS over HTTPS threat detection

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 preconditioning 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, $$<1$$ ms 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.