A survey on encrypted network traffic: A comprehensive survey of identification/classification techniques, challenges, and future directions

Abstract

Encrypted traffic detection and classification is a critical domain in network security, increasingly essential in an era of pervasive encryption. This survey paper delves into integrating advanced Machine Learning (ML) and Deep Learning (DL) techniques to address the challenges of robust encryption methods and dynamic network behaviors. Despite notable advancements, there remains a substantial gap in the operational application of these technologies, often constrained by scalability, efficiency, and adaptability to varied encryption standards. We critically review existing methodologies from 7 surveys and 82 related technical papers, highlight the shortcomings, and propose future research directions. Our analysis underscores the need to develop innovative, resource-efficient models that seamlessly adapt to new threats and encryption techniques without compromising performance. Additionally, we advocate for creating comprehensive datasets that merge encrypted and non-encrypted traffic to enhance model training and testing. This survey maps out the trajectory of recent developments and charts a course for future research that could significantly enhance encrypted traffic management and security capabilities.