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 lev