用于加密流量分析的生成式AI：合成数据集生成与分类器评估
Generative AI for Encrypted Traffic Analysis: Synthetic Dataset Generation and Classifier Evaluation
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中文总结 AI 辅助
该研究针对加密流量分析的数据集不平衡问题，利用生成式AI技术生成高质量合成加密流量数据集，补充真实数据训练分类器，性能达真实数据的93%，并提供完整代码。
中文摘要 AI 辅助
网络流量分析因加密通信面临重大挑战，主要在于无法查看数据包内容，且可用数据集存在固有不平衡问题，尤其是异常流量模式。本文探索生成式AI（GAI）技术以创建真实且平衡的合成加密流量数据集，方法包含特征分析、基于聚类的数据生成及全面的分类器评估，确保合成数据质量。研究表明，经恰当生成的合成数据可有效补充真实世界数据集，训练分类器时性能最高可达仅用真实数据训练的93%；所提方法保留关键统计属性与特征相关性，还能创建平衡数据集，解决网络安全数据中异常样本代表性不足的长期难题。此外，本文提供了工作中设计并实现的完整编程代码。
英文摘要
Network traffic analysis faces significant challenges with encrypted communications, primarily due to limited visibility into packet contents and the inherent imbalance in available datasets, particularly for anomalous traffic patterns. This paper addresses these challenges by exploring Generative AI (GAI) techniques to create realistic and balanced synthetic encrypted traffic datasets. Our approach incorporates feature analysis, clustering-based data generation, and comprehensive classifier evaluation to ensure synthetic data quality. We demonstrate that properly generated synthetic data can effectively supplement real- world datasets, achieving up to 93% performance when training classifiers compared to those trained on real data. The proposed methodology preserves critical statistical properties and feature correlations while enabling the creation of balanced datasets, ad- dressing the persistent challenge of anomaly underrepresentation in cybersecurity data. Along with the results we provide complete programming code designed and implemented in this work.
