Generative AI for Encrypted Traffic Analysis: Synthetic Dataset Generation and Classifier Evaluation

2026-08-10

AI Summary

This study addresses the critical challenges in encrypted traffic analysis—namely, data invisibility and severe class imbalance due to the scarcity of anomalous samples. To tackle these issues, this work introduces, for the first time, a systematic application of generative artificial intelligence to the domain, proposing a synthetic data generation method that preserves the statistical properties and feature correlations of original traffic. The approach integrates feature analysis, clustering guidance, and generative modeling to collaboratively construct a balanced dataset. Experimental results demonstrate that classifiers trained on the synthesized data achieve 93% of the performance of models trained on real data, substantially enhancing anomaly detection capabilities. The implementation code has been made publicly available.

Abstract

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.

Problem

• encrypted traffic analysis
• data imbalance
• anomalous traffic
• synthetic dataset
• network security

Innovation

• Generative AI
• Encrypted Traffic Analysis
• Synthetic Dataset Generation
• Data Imbalance
• Classifier Evaluation