Neuromorphic Quantum Adversarial Learning (NQAL): A Bio-Inspired Paradigm for DNS over HTTPS Threat Detection

Neuromorphic Quantum Adversarial Learning (NQAL): A Bio-Inspired Paradigm for DNS over HTTPS Threat Detection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Neuromorphic Quantum Adversarial Learning (NQAL): A Bio-Inspired Paradigm for DNS over HTTPS Threat Detection Basharat Ali This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6414048/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Dec, 2025 Read the published version in EURASIP Journal on Information Security → Version 1 posted 13 You are reading this latest preprint version 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. Network Security NQAL in Network Security Network Protocols Enhancing Network Security Enhancing DoH Protocol Security Threats Detection in Encrypted Network Cyber Attacks Detections Full Text Additional Declarations No competing interests reported. Supplementary Files NeuromorphicQuantumAdversarialLearningNQALABioInspiredParadigmforDNSoverHTTPSThreatDetection.zip Cite Share Download PDF Status: Published Journal Publication published 18 Dec, 2025 Read the published version in EURASIP Journal on Information Security → Version 1 posted Editorial decision: Revision requested 06 Jul, 2025 Reviews received at journal 25 Jun, 2025 Reviews received at journal 14 Jun, 2025 Reviews received at journal 03 Jun, 2025 Reviews received at journal 29 May, 2025 Reviewers agreed at journal 04 May, 2025 Reviewers agreed at journal 03 May, 2025 Reviewers agreed at journal 03 May, 2025 Reviewers agreed at journal 02 May, 2025 Reviewers invited by journal 28 Apr, 2025 Editor assigned by journal 28 Apr, 2025 Submission checks completed at journal 10 Apr, 2025 First submitted to journal 09 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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