Support Vector Machine Integrated with game-theoretic approach and genetic algorithm for the detection and classification of malware
Zolotukhin, M., & Hämäläinen, T. (2013). Support Vector Machine Integrated with game-theoretic approach and genetic algorithm for the detection and classification of malware. In IEEE Globecom 2013 Conference Proceedings : Big Security 2013, First International Workshop on Security and Privacy in Big Data (pp. 211-216). IEEE. IEEE Global Telecommunications Conference. https://doi.org/10.1109/GLOCOMW.2013.6824988
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IEEE Global Telecommunications ConferenceDate
2013Copyright
© Copyright 2014 IEEE. Article's final and definitive form has been published by IEEE.
Abstract.
—In the modern world, a rapid growth of mali-
cious software production has become one of the most signifi-
cant threats to the network security. Unfortunately, wides
pread
signature-based anti-malware strategies can not help to de
tect
malware unseen previously nor deal with code obfuscation te
ch-
niques employed by malware designers. In our study, the prob
lem
of malware detection and classification is solved by applyin
g a
data-mining-based approach that relies on supervised mach
ine-
learning. Executable files are presented in the form of byte a
nd
opcode sequences and n-gram models are employed to extract
essential features from these sequences. Feature vectors o
btained
are classified with the help of support vector classifiers int
egrated
with a genetic algorithm used to select the most essential fe
atures,
and a game-theory approach is applied to combine the classifi
ers
together. The proposed algorithm, ZSGSVM, is tested by usin
g a
set of byte and opcode sequences obtained from a set containi
ng
executable files of benign software and malware. As a result,
almost all malicious files are detected while the number of fa
lse
alarms remains very low.
...
Publisher
IEEEParent publication ISBN
978-1-4799-2851-4Conference
IEEE Globecom Workshops : International Workshop on Security and Privacy in Big DataIs part of publication
IEEE Globecom 2013 Conference Proceedings : Big Security 2013, First International Workshop on Security and Privacy in Big DataISSN Search the Publication Forum
1930-529XKeywords
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https://converis.jyu.fi/converis/portal/detail/Publication/23787836
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