计算机科学
恶意软件
卷积神经网络
人工智能
机器学习
可视化
双线性插值
鉴定(生物学)
数据挖掘
模式识别(心理学)
计算机安全
计算机视觉
植物
生物
作者
Sang Ni,Quan Qian,Rui Zhang
标识
DOI:10.1016/j.cose.2018.04.005
摘要
Currently, malware is one of the most serious threats to Internet security. In this paper we propose a malware classification algorithm that uses static features called MCSC (Malware Classification using SimHash and CNN) which converts the disassembled malware codes into gray images based on SimHash and then identifies their families by convolutional neural network. During this process, some methods such as multi-hash, major block selection and bilinear interpolation are used to improve the performance. Experimental results show that MCSC is very effective for malware family classification, even for those unevenly distributed samples. The classification accuracy can be 99.260% at best and 98.862% at average on a malware dataset of 10,805 samples which is higher than other compared algorithms. Moreover, for MCSC, on average, it just takes 1.41 s to recognize a new sample, which can meet the requirements in most of the practical applications.
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