Classifying Malware Using Convolutional Gated Neural Network

被引：0

作者：

Kim, Chang Hoon ^{[1
]}

Kabanga, Espoir K. ^{[1
]}

Kang, Sin-Jae ^{[1
]}

机构：

[1] Daegu Univ, Dept Comp & Informat Engn, Gyongsan, South Korea

来源：

2018 20TH INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATION TECHNOLOGY (ICACT) | 2018年

关键词：

Malware; Machine Learning; Neural Network; Deep Neural Network; CNN; Gated Recurrent Unit;

D O I：

暂无

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：

Malware or Malicious Software, are an important threat to information technology society. Deep Neural Network has been recently achieving a great performance for the tasks of malware detection and classification. In this paper, we propose a convolutional gated recurrent neural network model that is capable of classifying malware to their respective families. The model is applied to a set of malware divided into 9 different families and that have been proposed during the Microsoft Malware Classification Challenge in 2015. The model shows an accuracy of 92.6% on the available dataset.

引用

页码：40 / 44

页数：5