Next-generation AI for advanced threat detection and security enhancement in DNS over HTTPS

Abstract

The widespread adoption of DNS over HTTPS(DoH) has inaugurated a new paradigm of network privacy through the encryption of DNS queries; paradoxically, this very mechanism has been weaponized by malicious actors to orchestrate convert cyberattacks ranging from polymorphic malware delivery and data exfiltration to command-and-control (C2) operations. Classic signature-based solutions that rely on static security policies and packet-depth inspection are rendered useless in the face of encrypted DoH traffic, and today’s AI-driven defense solutions typically fail to achieve adversarial robustness, explainability, and real-time scalability. Bridging these gaps, this paper proposes an AI framework that integrates the best practices in machine learning together with secure execution environments to offer resilience, transparency, and low-latency DoH threat detection. Specifically, Capsule Networks (CapsNets) are used to learn hierarchical traffic flow patterns, Graph Transformers to uncover temporal anomalies, and Contrastive Self-Supervised Learning (CSSL) to leverage massive unlabeled datasets. Adversarial robustness is reinforced through perturbation-aware training and mutation-driven fuzzing simulations, while interpretability is enhanced via SHAP and LIME, rendering AI decision-making processes more intelligible to analysts. A distributed Apache Flink/Kafka pipeline enables real-time processing of DoH streams at scale, reducing detection latency by 50% compared to batch-oriented systems. Furthermore, Trusted Execution Environments(TEEs) safeguard model inference against tempering, mitigating insider threats and runtime exploitation. Empirical evaluation on the doh_real_world_2022 dataset demonstrates 99.1% detection accuracy with CapsNets, 98.8% with Graph Transformers, and an 80% improvement in adversarial resilience. These developments collectively propel the discipline of encrypted traffic analysis and establish a benchmark for safeguarding cybersecurity protocols such as QUIC and HTTP/3 that are gaining traction. The findings validate the feasibility of AI-driven, privacy-augmented security systems during an era of escalating cyber-attacks and demands algorithmic transparency.