Generative AI for Encrypted Traffic Analysis: Synthetic Dataset Generation and Classifier Evaluation
2026-08-10 • Cryptography and Security
Cryptography and Security
AI summaryⓘ
The authors studied how to improve analyzing network traffic that is encrypted and hard to see inside. They used Generative AI to make realistic fake traffic data, especially to fix the problem of not having enough examples of unusual, suspicious traffic. Their method carefully mimics real data patterns and helps balance the dataset, which improved how well classifiers learned—almost as good as using only real data. They also shared their full code to help others use their approach.
Network traffic analysisEncrypted communicationsGenerative AISynthetic data generationData imbalanceAnomaly detectionFeature analysisClusteringClassifier evaluation
Authors
Harshil Patel, Himanshu Garg, Aswani Kumar Cherukuri
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.