AI-based Traffic Modeling for Network Security and Privacy: Challenges Ahead

2025/03/28 by Dinil Mon Divakaran, Divakaran, Dinil Mon · 1 citation
Computer Science · Social Sciences · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Internet Traffic Analysis and Secure E-voting #Legal and Policy Issues #Network Security and Intrusion Detection

paper · pdf · doi:10.48550/arxiv.2503.22161

openalex publication_date 2025/03/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/02

Abstract

Network traffic analysis using AI (machine learning and deep learning) models made significant progress over the past decades. Traffic analysis addresses various challenging problems in network security, ranging from detection of anomalies and attacks to countering of Internet censorship. AI models are also developed to expose user privacy risks as demonstrated by the research works on fingerprinting of user-visiting websites, IoT devices, and different applications, even when payloads are encrypted. Despite these advancements, significant challenges remain in the domain of network traffic analysis to effectively secure our networks from evolving threats and attacks. After briefly reviewing the relevant tasks and recent AI models for traffic analysis, we discuss the challenges that lie ahead.

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