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End-node Fingerprinting for Malware Detection on HTTPS Data
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SYSNO ASEP 0507114 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title End-node Fingerprinting for Malware Detection on HTTPS Data Author(s) Komárek, T. (CZ)
Somol, Petr (UTIA-B) RIDNumber of authors 2 Article number 77 Source Title Proceedings of the 12th International Conference on Availability, Reliability and Security (ARES'17). - New York : ACM, 2017 - ISBN 978-1-4503-5257-4 Pages s. 1-7 Number of pages 7 s. Publication form Print - P Action the 12th International Conference on Availability, Reliability and Security (ARES'17) Event date 29.08.2017 - 01.09.2017 VEvent location Reggio Calabria Country IT - Italy Event type WRD Language eng - English Country US - United States Keywords HTTPS data ; Malware detection ; Supervised learning Subject RIV BC - Control Systems Theory OECD category Robotics and automatic control Institutional support UTIA-B - RVO:67985556 UT WOS 000426964900077 EID SCOPUS 85030325858 DOI 10.1145/3098954.3107007 Annotation One of the current challenges in network intrusion detection research is the malware communicating over HTTPS protocol. Usually the task is to detect infected end-nodes with this type of malware by monitoring network traffc. The challenge lies in a very limited number of weak features that can be extracted from the network traffc capture of encrypted HTTP communication. This paper suggests a novel fingerprinting method that addresses this
problem by building a higher-level end-node representation on top of the weak features. Conducted large-scale experiments on real network data show superior performance of the proposed method over the state-of-the-art solution in terms of both a lower number of produced false alarms (precision) and a higher number of detected infections (recall).Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2020
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