Original title: Detection of Malicious Network Traffic Behavior Using JA3 Fingerprints
Authors: Novák, Pavel ; Oujezský, Václav
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract: This paper presents a novel approach for classifying spoof network traffic based on JA3 fingerprint clustering. In particular, it concerns the detection of so-called zero-day malware. The proposed method does not work with known JA3 hashes. However, it compares the JA3 fingerprint of captured traffic with JA3 fingerprints of traffic with predefined criteria, such as the use of current cipher suites or protocol, for classification.
Keywords: clustering, detection, JA3, JA3s, malware
Host item entry: Proceedings II of the 28st Conference STUDENT EEICT 2022: Selected papers, ISBN 978-80-214-6030-0

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: http://hdl.handle.net/11012/208635

Permalink: http://www.nusl.cz/ntk/nusl-511904


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Universities and colleges > Public universities > Brno University of Technology
Conference materials > Papers