Unveiling Hidden Patterns: Self-Similarity and Entropy for Robust Encrypted DNS Traffic Security

Abstract The increasing complexity and volume of modern network traffic, specifically within the context of encrypted Domain Name System (DNS) protocols, particularly DNS over HTTPS (DoH), pose significant challenges to traditional traffic analysis methods, making it difficult to discern legitimate activity from covert or malicious communications. This paper explores the intrinsic self-similarity and long-term memory properties of encrypted DNS traffic, employing multiple statistical methods for