Experience Report: Using JA4+ Fingerprints for Malware Detection in Encrypted Traffic

Detection of malware communications is limited due to encryption. Malware control, updates, and distribution are encapsulated in TLS tunnels, making it difficult to distinguish between malicious and benign transmissions. One way, how to detect malware communication, is to analyze the TLS handshake and obtain so-called JA4+ fingerprints. This report analyses the effectiveness of JA4+ fingerprints for malware detection, focusing specifically on the JA4, JA4S, and JA4X fingerprints and their accuracy. It examines the process of creating malware fingerprints and explores the uniqueness of these fingerprints across different malware families and their ability to distinguish between malicious and benign applications. By examining the overlap and uniqueness, the study evaluates the effectiveness of using JA4+ fingerprints to detect malware in encrypted communications.

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Publication Details

Published
2024-10-28
DOI
https://doi.org/10.23919/cnsm62983.2024.10814358
Citations
1
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
0.35
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article

Experience Report: Using JA4+ Fingerprints for Malware Detection in Encrypted Traffic

Ondřej Ryšavý, Petr Matoušek, Ivana Burgetová
1 citations
Advanced Malware Detection Techniques
0.35
article

Experience Report: Using JA4+ Fingerprints for Malware Detection in Encrypted Traffic

Ondřej Ryšavý, Petr Matoušek, Ivana Burgetová
article en
1 citations

Abstract

Detection of malware communications is limited due to encryption. Malware control, updates, and distribution are encapsulated in TLS tunnels, making it difficult to distinguish between malicious and benign transmissions. One way, how to detect malware communication, is to analyze the TLS handshake and obtain so-called JA4+ fingerprints. This report analyses the effectiveness of JA4+ fingerprints for malware detection, focusing specifically on the JA4, JA4S, and JA4X fingerprints and their accuracy. It examines the process of creating malware fingerprints and explores the uniqueness of these fingerprints across different malware families and their ability to distinguish between malicious and benign applications. By examining the overlap and uniqueness, the study evaluates the effectiveness of using JA4+ fingerprints to detect malware in encrypted communications.

Brno University of Technology (CZ)
Openalex Percentile: Top 40%
Advanced Malware Detection Techniques
0.35
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Experience Report: Using JA4+ Fingerprints for Malware Detection in Encrypted Traffic — Ondřej Ryšavý, Petr Matoušek, et al. · (2024) | TGRS Research Map | TGRS