Traffic Modeling for Network Security and Privacy: Challenges Ahead

Abstract

Traffic analysis using machine learning and deep learning models has made significant progress over the past decades. These models address various tasks in network security and privacy, including detection of anomalies and attacks, countering censorship, etc. They also reveal privacy risks to users as demonstrated by the research on LLM token inference as well as fingerprinting (and counter-fingerprinting) of user-visiting websites, IoT devices, and different applications. However, challenges remain in securing our networks from threats and attacks. After briefly reviewing the tasks and recent ML models in network security and privacy, we discuss the challenges that lie ahead.

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