Classification of Methamorphic Malware with Deep Learning(LSTM)

被引：14

作者：

Yaz, Ahmet Faruk ^{[1
]}

Catak, Ferhat Ozgur ^{[2
]}

Gul, Ensar ^{[1
]}

机构：

[1] Istanbul Sehir Univ, Bilgi Guvenligi Muhendisligi, Istanbul, Turkey

[2] TUBITAK, BILGEM, Kocaeli, Turkey

来源：

2019 27TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2019年

关键词：

Metamorphic malware; Windows API; deep learning; LSTM;

D O I：

10.1109/siu.2019.8806571

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页数：4

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