Online Malware Detection with Variational Autoencoders
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00533909" target="_blank" >RIV/67985807:_____/20:00533909 - isvavai.cz</a>
Result on the web
<a href="http://ceur-ws.org/Vol-2718/paper19.pdf" target="_blank" >http://ceur-ws.org/Vol-2718/paper19.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Online Malware Detection with Variational Autoencoders
Original language description
This paper studies the application of variational autoencoders (VAEs) to online learning from malware detection data. To this end, it employs a large real-world dataset of anonymized highdimensional data collected during 375 consecutive weeks. Several VAEs were trained on selected subsets of this time series and subsequently tested on different subsets. For the assessment of their performance, the accuracy metric is complemented with the Wasserstein distance. In addition, the influence of different kinds of data normalization on the VAE performance has been investigated. Finally, the combinations of a VAE with two multi-layer perceptorns (MLPs) have been investigated, which has lead to the surprising result that the impact of such a combination on malware detection is positive for a simple and superficially optimized MLP, but negative for a complex and well optimized one.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA18-18080S" target="_blank" >GA18-18080S: Fusion-Based Knowledge Discovery in Human Activity Data</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 20th Conference Information Technologies - Applications and Theory
ISBN
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ISSN
1613-0073
e-ISSN
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Number of pages
8
Pages from-to
122-129
Publisher name
Technical University & CreateSpace Independent Publishing
Place of publication
Aachen
Event location
Oravská Lesná
Event date
Sep 18, 2020
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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