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Classification of Methamorphic Malware with Deep Learning (LSTM)

Catak, Ferhat Özgur; Yazi, Ahmet Faruk; Gul, Ensar
Chapter
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URI
https://hdl.handle.net/11250/2650279
Date
2019
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  • Institutt for informasjonssikkerhet og kommunikasjonsteknologi [2772]
  • Publikasjoner fra CRIStin - NTNU [41778]
Original version
10.1109/SIU.2019.8806571
Abstract
Nowadays, anti-virus applications using traditional signature-based detection methods fail to detect metamorphic malware. For this reason, recent studies on the detection and classification of malicious software address the behavior of malware. In this study, an LSTM based classification method was developed by using API calls of 8 different types of real malware. With this method, the behaviors of the malware types on the operating system are modeled.
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Series
IEEE Conference Publication;

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