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Detecting Malicious Executable Files Based on Static–Dynamic Analysis Using Machine Learning

Detecting Malicious Executable Files Based on Static–Dynamic Analysis Using Machine Learning In current operating systems, executable files are used to solve various problems, which in turn can be either benign (perform only necessary actions) or malicious (the main purpose of which is to perform destructive actions in relation to the system). Thus, malware is a program used for unauthorized access to information and/or impact on information or resources of an automated information system. Here, the problem of determining the types of executable files and detecting malware is solved. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automatic Control and Computer Sciences Springer Journals

Detecting Malicious Executable Files Based on Static–Dynamic Analysis Using Machine Learning

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Publisher
Springer Journals
Copyright
Copyright © Allerton Press, Inc. 2022. ISSN 0146-4116, Automatic Control and Computer Sciences, 2022, Vol. 56, No. 8, pp. 852–864. © Allerton Press, Inc., 2022. Russian Text © The Author(s), 2022, published in Problemy Informatsionnoi Bezopasnosti, Komp’yuternye Sistemy.
ISSN
0146-4116
eISSN
1558-108X
DOI
10.3103/s0146411622080120
Publisher site
See Article on Publisher Site

Abstract

In current operating systems, executable files are used to solve various problems, which in turn can be either benign (perform only necessary actions) or malicious (the main purpose of which is to perform destructive actions in relation to the system). Thus, malware is a program used for unauthorized access to information and/or impact on information or resources of an automated information system. Here, the problem of determining the types of executable files and detecting malware is solved.

Journal

Automatic Control and Computer Sciences – Springer Journals

Published: Dec 1, 2022

Keywords: information security systems; malware detection; static–dynamic analysis; feature selection of program behavior parameters

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