A graph representation framework for encrypted network traffic classification

• Published in: Computers & security Vol. 148; p. 104134
• Main Authors: Okonkwo, Zulu, Foo, Ernest, Hou, Zhe, Li, Qinyi, Jadidi, Zahra
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-01-2025
• Subjects: Encryption; Graphs; Internet security; Network traffic classification; Neural Networks; Neural Networks; Graphs; Encryption; Internet security; Network traffic classification
• ISSN: 0167-4048
• DOI: 10.1016/j.cose.2024.104134

Network Traffic Classification (NTC) is crucial for ensuring internet security, but encryption presents significant challenges to this task. While Machine Learning (ML) and Deep Learning (DL) methods have shown promise, issues such as limited representativeness leading to sub-optimal generalizations and performance remain prevalent. These problems become more pronounced with advanced obfuscation, network security, and privacy technologies, indicating a need for improved model robustness. To address these issues, we focus on feature extraction and representation in NTC by leveraging the expressive power of graphs to represent network traffic at various granularity levels. By modeling network traffic as interconnected graphs, we can analyze both flow-level and packet-level data. Our graph representation method for encrypted NTC effectively preserves crucial information despite encryption and obfuscation. We enhance the robustness of our approach by using cosine similarity to exploit correlations between encrypted network flows and packets, defining relationships between abstract entities. This graph structure enables the creation of structural embeddings that accurately define network traffic across different encryption levels. Our end-to-end process demonstrates significant improvements where traditional NTC methods struggle, such as in Tor classification, which employs anonymization to further obfuscate traffic. Our packet-level classification approach consistently outperforms existing methods, achieving accuracies exceeding 96%.