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A Multiclass Detection System for Android Malicious Apps Based on Color Image Features
Joint Authors
Gao, Fei
Zhang, Hua
Guo, Jing
Qin, Jiawei
Zhang, Boan
Yan, Hanbing
Wang, Senmiao
Hu, Yangye
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-21, 21 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-16
Country of Publication
Egypt
No. of Pages
21
Main Subjects
Information Technology and Computer Science
Abstract EN
The visual recognition of Android malicious applications (Apps) is mainly focused on the binary classification using grayscale images, while the multiclassification of malicious App families is rarely studied.
If we can visualize the Android malicious Apps as color images, we will get more features than using grayscale images.
In this paper, a method of color visualization for Android Apps is proposed and implemented.
Based on this, combined with deep learning models, a multiclassifier for the Android malicious App families is implemented, which can classify 10 common malicious App families.
In order to better understand the behavioral characteristics of malicious Apps, we conduct a comprehensive manual analysis for a large number of malicious Apps and summarize 1695 malicious behavior characteristics as customized features.
Compared with the App classifier based on the grayscale visualization method, it is verified that the classifier using the color visualization method can achieve better classification results.
We use four types of Android App features: classes.dex file, sets of class names, APIs, and customized features as input for App visualization.
According to the experimental results, we find out that using the customized features as the color visualization input features can achieve the highest detection accuracy rate, which is 96% in the ten malicious families.
American Psychological Association (APA)
Zhang, Hua& Qin, Jiawei& Zhang, Boan& Yan, Hanbing& Guo, Jing& Gao, Fei…[et al.]. 2020. A Multiclass Detection System for Android Malicious Apps Based on Color Image Features. Wireless Communications and Mobile Computing،Vol. 2020, no. 2020, pp.1-21.
https://search.emarefa.net/detail/BIM-1214860
Modern Language Association (MLA)
Zhang, Hua…[et al.]. A Multiclass Detection System for Android Malicious Apps Based on Color Image Features. Wireless Communications and Mobile Computing No. 2020 (2020), pp.1-21.
https://search.emarefa.net/detail/BIM-1214860
American Medical Association (AMA)
Zhang, Hua& Qin, Jiawei& Zhang, Boan& Yan, Hanbing& Guo, Jing& Gao, Fei…[et al.]. A Multiclass Detection System for Android Malicious Apps Based on Color Image Features. Wireless Communications and Mobile Computing. 2020. Vol. 2020, no. 2020, pp.1-21.
https://search.emarefa.net/detail/BIM-1214860
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214860