Towards Identification of Network Applications in Encrypted Traffic

Towards Identification of Network Applications in Encrypted Traffic

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Network traffic monitoring for security threat detection and network performance management is challenging because most communications are protected by encryption. This paper addresses the problem of identifying applications associated with Transport Layer Security (TLS) network connections. We evaluate three primary approaches to classifying TLS traffic: fingerprinting methods, SNI-based identification, and machine learning-based classifiers. Each method has strengths and limitations: fingerprinting relies on a regularly updated database of known hashes, SNI is vulnerable to obfuscation or missing information, and an AI technique such as machine learning requires sufficient labelled training data. The comparison of these methods that we present highlights the challenges of identifying individual applications, as TLS properties are significantly shared across applications. The simpler task of identifying a collection of candidate applications still provides valuable insights for network monitoring and can be achieved with high accuracy by all methods considered. Finally, we suggest practical use cases and identify future research directions to further improve application identification methods.

Network traffic monitoring for security threat detection and network performance management is challenging because most communications are protected by encryption. This paper addresses the problem of identifying applications associated with Transport Layer Security (TLS) network connections. We evaluate three primary approaches to classifying TLS traffic: fingerprinting methods, SNI-based identification, and machine learning-based classifiers. Each method has strengths and limitations: fingerprinting relies on a regularly updated database of known hashes, SNI is vulnerable to obfuscation or missing information, and an AI technique such as machine learning requires sufficient labelled training data. The comparison of these methods that we present highlights the challenges of identifying individual applications, as TLS properties are significantly shared across applications. The simpler task of identifying a collection of candidate applications still provides valuable insights for network monitoring and can be achieved with high accuracy by all methods considered. Finally, we suggest practical use cases and identify future research directions to further improve application identification methods.

TLS fingerprinting, JA4, encrypted traffic, application identification, machine learning

TLS fingerprinting, JA4, encrypted traffic, application identification, machine learning

BURGETOVÁ, I.; RYŠAVÝ, O.; MATOUŠEK, P.

2026

04.12.2024

IEEE Communications Society

Paris

979-8-3315-3411-0

The Proceedings of the 8th Cyber Security in Networking Conference (CSNet 2024)

8

213

221

9

https://www.fit.vut.cz/research/publication/13289/

CSNet_2024
Towards_Identification_of_Network_Applications_in_Encrypted_Traffic