Image Visualization based Malware Detection

被引：0
|
作者：
Kancherla, Kesav [1 ]
Mukkamala, Srinivas [1 ]
机构：
[1] New Mexico Inst Min & Technol, Computat Anal & Network Enterprise Solut CAaNES, Inst Complex Addit Syst & Anal, Socorro, NM 87801 USA
来源：
2013 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN CYBER SECURITY (CICS) | 2013年
关键词：
Malware Detection; Machine Learning; Support Vector Machines (SVMs); Textures based Features;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Malware detection is one of the challenging tasks in Cyber security. The advent of code obfuscation, metamorphic malware, packers and zero day attacks has made malware detection a challenging task. In this paper we present a visualization based approach for malware detection. First the executable is converted to a gray-scale image called byteplot. Later we extract low level features like intensity based and texture based features. We apply computationally intelligent techniques for malware detection using these features. In this work we used Support Vector Machines (SVMs) and obtained an accuracy of 95% on a dataset containing 25000 malware and 12000 benign samples.
引用
收藏
页码：40 / 44
页数：5
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