Two Semi-supervised Approaches to Malware Detection with Neural Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F20%3A00342838" target="_blank" >RIV/68407700:21240/20:00342838 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Two Semi-supervised Approaches to Malware Detection with Neural Networks
Original language description
Semi-supervised learning is characterized by using the additional information from the unlabeled data. In this paper, we compare two semi-supervised algorithms for deep neural networks on a large real-world malware dataset. Specifically, we evaluate the performance of a rather straightforward method called Pseudo-labeling, which uses unlabeled samples, classified with high confidence, as if they were the actual labels. The second approach is based on an idea to increase the consistency of the network’s prediction under altered circumstances. We implemented such an algorithm called Π-model, which compares outputs with different data augmentation and different dropout setting. As a baseline, we also provide results of the same deep network, trained in the fully supervised mode using only the labeled data. We analyze the prediction accuracy of the algorithms in relation to the size of the labeled part of the training dataset.
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
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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 (ITAT 2020)
ISBN
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ISSN
1613-0073
e-ISSN
1613-0073
Number of pages
10
Pages from-to
176-185
Publisher name
CEUR Workshop Proceedings
Place of publication
Aachen
Event location
hotel Tyrapol, Oravská Lesná
Event date
Sep 18, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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