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Paper 1

Proactive and privacy-Preserving defense for DNS over HTTPS via federated AI attestation (PAFA-DoH).

Basharat Ali, Guihai Chen

Year

2026

Journal

Neural networks : the official journal of the International Neural Network Society

DOI

10.1016/j.neunet.2025.108343

arXiv

-

DNS over HTTPS(DoH) improves privacy but still admits tunneling, resolver manipulation, and side-channel leakage. We present PAFA-DoH, a practical defense that unifies Federated AI, Quantum-resilient cryptography, and neuromorphic anomaly detection in a single, privacy-preserving framework. The system learns from encrypted traffic via adversarial federated learning with homomorphic inference, so models improve collaboratively without exposing raw queries. To surface hard-to-see behaviors(e.g., periodic C2 beacons), we extract topological signature from flows using persistence-diagram-based topological data analysis. Real-time detection is executed on Spiking Neural Network (SNN), running on neuromorphic hardware, delivering high throughput at low energy cost. Trust is enforced with an AI-assisted zero-knowledge attestation scheme that integrates ZK-SNARKs/STARKs to continuously verify client and resolver integrity without revealing metadata. We evaluate PAFA-DoH on a custom testbed that includes quantum-adversarial traffic, compromised-client simulations, and emulated rogue resolvers. Our Model achieves  ≥ 99.20 % malicious-activity detection accuracy,  < 100ms ZK verification, and  < 150 ms added handshake latency, while federated models converge within 10 training epochs. By combining privacy-preserving learning, verifiable trust, and event-driven analytics PAFA-DoH offers deployable path to zero-trust, post quantum-hardened protection for encrypted DNS.

Paper 2

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