Next-Generation DNS RPZ for Automated Threat Intelligence, Risk-Aware Filtering, and User-Centric Security

The Domain Name System (DNS) remains a critical attack vector exploited by adversaries for command-and-control (C2) communication, data exfiltration, and phishing campaigns. DNS Response Policy Zones (RPZ) have emerged as an effective defense mechanism by enabling the redirection or blocking of queries to malicious domains. However, current RPZ implementations encounter significant challenges related to scalability, adaptability, and user awareness, often resulting in static policies, delayed updates, and a high incidence of false positives. To address these limitations, this paper proposes an enhanced RPZ framework that integrates adaptive threat intelligence, machine learning-driven dynamic policy updates, and user-context-aware security controls. The proposed framework incorporates real-time ingestion of diverse threat intelligence feeds, automated scoring mechanisms to prioritize indicators based on risk attributes such as entropy, domain age, domain generation algorithm (DGA) likelihood, and reputation history, as well as machine learning techniques for continuous refinement of blocklists. Additionally, the framework presents novel methods for minimizing false positives by leveraging user behavior analytics and contextual policy enforcement across heterogeneous network environments. Experimental validation demonstrates the system’s ability to scale to large DNS traffic environments through controlled high-load tests, while effectively reducing malicious query resolutions and maintaining accurate policy adjustments. By combining automation, risk-based intelligence, and user-centric adaptability, the proposed model advances DNS security beyond static defenses and offers a proactive, scalable, and context-sensitive approach to mitigating emerging DNS threats.