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Discriminative Models for Multi-instance Problems with Tree Structure
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SYSNO ASEP 0463379 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Discriminative Models for Multi-instance Problems with Tree Structure Author(s) Somol, Petr (UTIA-B) RID
Pevný, T. (CZ)Number of authors 2 Source Title Proceedings of the 9th ACM Workshop on Artificial Intelligence and Security 2016. - New York : ACM, 2016 - ISBN 978-1-4503-4573-6 Number of pages 9 s. Publication form Online - E Action 9th ACM Workshop on Artificial Intelligence and Security Event date 28.10.2016 VEvent location Vienna Country AT - Austria Event type WRD Language eng - English Country US - United States Keywords Neural netwrok ; User modeling ; Malware detection ; Big data ; Learning indicators of compromise Subject RIV IN - Informatics, Computer Science Institutional support UTIA-B - RVO:67985556 UT WOS 000391051600008 EID SCOPUS 85001945953 DOI 10.1145/2996758.2996761 Annotation Modelling network traffic is gaining importance to counter modern security threats of ever increasing sophistication. It is though surprisingly difficult and costly to construct reliable classifiers on top of telemetry data due to the variety and complexity of signals that no human can manage to interpret in full. Obtaining training data with sufficiently large and variable body of labels can thus be seen as a prohibitive problem. The goal of this work is to detect infected computers by observing their HTTP(S) traffic collected from network sensors, which are typically proxy servers or network firewalls, while relying on only minimal human input in the model training phase. We propose a discriminative model that makes decisions based on a computer’s all traf.c observed during a predefined time window (5 minutes in our case). Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2017
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