Android Malware Detection Based on a Hybrid Deep Learning Model
Joint Authors
Lu, Tianliang
Du, Yanhui
Ouyang, Li
Chen, Qiuyu
Wang, Xirui
Source
Security and Communication Networks
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-28
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Information Technology and Computer Science
Abstract EN
In recent years, the number of malware on the Android platform has been increasing, and with the widespread use of code obfuscation technology, the accuracy of antivirus software and traditional detection algorithms is low.
Current state-of-the-art research shows that researchers started applying deep learning methods for malware detection.
We proposed an Android malware detection algorithm based on a hybrid deep learning model which combines deep belief network (DBN) and gate recurrent unit (GRU).
First of all, analyze the Android malware; in addition to extracting static features, dynamic behavioral features with strong antiobfuscation ability are also extracted.
Then, build a hybrid deep learning model for Android malware detection.
Because the static features are relatively independent, the DBN is used to process the static features.
Because the dynamic features have temporal correlation, the GRU is used to process the dynamic feature sequence.
Finally, the training results of DBN and GRU are input into the BP neural network, and the final classification results are output.
Experimental results show that, compared with the traditional machine learning algorithms, the Android malware detection model based on hybrid deep learning algorithms has a higher detection accuracy, and it also has a better detection effect on obfuscated malware.
American Psychological Association (APA)
Lu, Tianliang& Du, Yanhui& Ouyang, Li& Chen, Qiuyu& Wang, Xirui. 2020. Android Malware Detection Based on a Hybrid Deep Learning Model. Security and Communication Networks،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1208784
Modern Language Association (MLA)
Lu, Tianliang…[et al.]. Android Malware Detection Based on a Hybrid Deep Learning Model. Security and Communication Networks No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1208784
American Medical Association (AMA)
Lu, Tianliang& Du, Yanhui& Ouyang, Li& Chen, Qiuyu& Wang, Xirui. Android Malware Detection Based on a Hybrid Deep Learning Model. Security and Communication Networks. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1208784
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208784