Online Malware Detection with Variational Autoencoders

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

Online Malware Detection with Variational Autoencoders

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Online Malware Detection with Variational Autoencoders

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA18-18080S" target="_blank" >GA18-18080S: Objevování znalostí v datech o aktivitě člověka založené na fúzi</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 20th Conference Information Technologies - Applications and Theory

ISBN

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ISSN

1613-0073

e-ISSN

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Počet stran výsledku

8

Strana od-do

122-129

Název nakladatele

Technical University & CreateSpace Independent Publishing

Místo vydání

Aachen

Místo konání akce

Oravská Lesná

Datum konání akce

18. 9. 2020

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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