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Malware Detection by HTTPS Traffic Analysis

  • In order to evade detection by network-traffic analysis, a growing proportion of malware uses the encrypted HTTPS protocol. We explore the problem of detecting malware on client computers based on HTTPS traffic analysis. In this setting, malware has to be detected based on the host IP address, ports, timestamp, and data volume information of TCP/IP packets that are sent and received by all the applications on the client. We develop a scalable protocol that allows us to collect network flows of known malicious and benign applications as training data and derive a malware-detection method based on a neural networks and sequence classification. We study the method's ability to detect known and new, unknown malware in a large-scale empirical study.

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Author:Paul PrasseGND, Gerrit Gruben, Lukas Machlika, Tomas Pevny, Michal Sofka, Tobias Scheffer
URN:urn:nbn:de:kobv:517-opus4-100942
Document Type:Preprint
Language:English
Date of first Publication:2017/01/23
Year of Completion:2016
Publishing Institution:Universität Potsdam
Release Date:2017/01/23
Tag:computer security; machine learning
Pagenumber:10
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):License LogoKeine Nutzungslizenz vergeben - es gilt das deutsche Urheberrecht