The global transition toward end-to-end encryption, driven by protocols such as TLS 1.3, HTTP/3, and DNS-over-HTTPS, has fundamentally altered the cybersecurity landscape. While encryption is essential for safeguarding user privacy and data integrity, it has simultaneously created a \\\"blind spot\\\" for traditional security infrastructure. Malicious actors increasingly leverage encrypted channels to conceal command-and-control (C2) communications, exfiltrate sensitive data, and deliver malware payloads, effectively bypassing legacy Deep Packet Inspection (DPI) tools. This review explores the paradigm shift toward Machine Learning (ML)-based anomaly detection as a solution to this visibility crisis. By focusing on side-channel telemetry—such as packet timing, size distributions, and byte-level patterns—rather than plaintext payloads, ML models can identify malicious intent without decrypting the traffic. This article categorizes current methodologies, including the use of Convolutional Neural Networks (CNNs) for spatial feature extraction from traffic headers and Long Short-Term Memory (LSTM) networks for temporal sequence modeling. We examine the critical role of feature engineering in transforming raw encrypted streams into actionable intelligence and discuss the integration of these models into high-speed network environments. Furthermore, the review addresses the challenges of data imbalance, the emergence of adversarial evasion techniques, and the necessity for explainable AI in security operations. By synthesizing recent research breakthroughs and industrial applications, this paper provides a strategic roadmap for building resilient, privacy-preserving detection systems that maintain security in an increasingly opaque digital ecosystem.
Nadeesha Fernando (Sun,) studied this question.