Open AccessVisualization Feature and CNN Based Homology Classification of Malicious Code
Author(s) -
Chu Qianfeng,
Liu Gongshen,
Zhu Xinyu
Publication year - 2020
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2019.11.005
Subject(s) - computer science , code (set theory) , visualization , convolutional neural network , homology (biology) , convolution (computer science) , artificial intelligence , data mining , artificial neural network , pattern recognition (psychology) , programming language , biochemistry , chemistry , set (abstract data type) , gene
The malicious code brings a serious security threat. Researchers have found that many new types of malicious code are variants of the existing one. The homology classification of the unknown malicious code can find its corresponding family in which all the code share inherent similarities from the database, so that the defenders can make rapid response and processing. We use the algorithm of malicious code visualization to translate the homology classification problem into the image classification problem. A convolution neural network for malicious code image is constructed. We train it to complete the malicious code homology classification on two different datasets. The results show that our work outperforms most of existing work with the accuracy of 98.60%.