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.

翻译：基于机器学习和深度学习模型的流量分析在过去数十年间取得了显著进展。这些模型处理网络安全与隐私领域的多项任务，包括异常与攻击检测、反审查等。同时，如大语言模型（LLM）令牌推断、用户访问网站指纹识别（及反指纹识别）、物联网设备与各类应用指纹识别等研究所示，这些模型也揭示了用户面临的隐私风险。然而，在保护网络免受威胁与攻击方面，挑战依然存在。本文在简要回顾网络安全与隐私领域的任务及近期机器学习模型后，探讨了未来面临的挑战。