Decentralized DNS Models for Secure, AI-Backed Content Delivery Networks

Abstract

The Domain Name System (DNS) is the foundational naming infrastructure of the Internet, translating human‐readable domain names into machine‐readable IP addresses. Despite its critical role, the traditional DNS architecture—characterized by hierarchical, centralized name servers—suffers from inherent vulnerabilities that undermine the security, availability, and performance of downstream services such as Content Delivery Networks (CDNs). Central points of failure can be exploited by Distributed Denial of Service (DDoS) attackers, and cache poisoning incidents can redirect legitimate traffic to malicious endpoints. In response, decentralized DNS models, leveraging blockchain and peer‑to‑peer (P2P) distributed hash tables (DHTs), have emerged to eliminate single points of failure, provide data immutability, and resist censorship. However, decentralization alone does not fully address performance optimization or intelligent threat mitigation. To close this gap, we propose an integrated framework that couples decentralized DNS architectures with artificial intelligence (AI) modules for real‑time anomaly detection and dynamic caching strategies. We implement two decentralized DNS prototypes—a blockchain‑based system using Ethereum Name Service principles and a Kademlia‑inspired P2P DHT system—and embed AI components: a random forest classifier for DNS traffic anomaly detection and a deep Q‑network agent for cache pre‑fetching. Through a comprehensive ns‑3 simulation of a global CDN spanning 50 edge servers and realistic Internet traffic patterns (including Alexa Top 1 Million requests and periodic high‑volume DDoS bursts), we collect metrics on DNS lookup latency, DDoS mitigation efficacy, and cache hit ratios. Statistical analysis over 1,000 trials with 95% confidence intervals reveals that our AI‑backed decentralized models reduce average DNS lookup latency by 25–29%, improve DDoS mitigation from 40% to over 90%, and enhance cache hit ratios by 12–15% compared to the centralized DNS baseline. These quantifiable benefits affirm the viability of combining decentralization with AI to secure and accelerate CDN operations. We conclude by discussing practical deployment considerations, such as blockchain transaction throughput, peer authentication mechanisms, and cross‑domain interoperability, and outline future research directions in federated learning, economic incentive designs, and quantum‑resilience for next‑generation DNS systems.