Research on Malware Detection Technology for Mobile Terminals Based on API Call Sequence

Ye Yao, Yian Zhu (), Yao Jia, Xianchen Shi, Lixiang Zhang, Dong Zhong and Junhua Duan
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Ye Yao: School of Computer Science, Northwest University of Technology, Xi’an 710072, China
Yian Zhu: School of Computer Science, Northwest University of Technology, Xi’an 710072, China
Yao Jia: School of Computer Science, Northwest University of Technology, Xi’an 710072, China
Xianchen Shi: School of Computer Science, Northwest University of Technology, Xi’an 710072, China
Lixiang Zhang: School of Computer Science, Northwest University of Technology, Xi’an 710072, China
Dong Zhong: School of Computer Science, Northwest University of Technology, Xi’an 710072, China
Junhua Duan: School of Computer Science, Northwest University of Technology, Xi’an 710072, China

Mathematics, 2023, vol. 12, issue 1, 1-26

Abstract: With the development of the Internet, the types and quantities of malware have grown rapidly, and how to identify unknown malware is becoming a new challenge. The traditional malware detection method based on fixed features is becoming more and more difficult. In order to improve detection accuracy and efficiency for mobile terminals, this paper proposed a malware detection method for mobile terminals based on application programming interface (API) call sequence, which was characterized by the API call sequence and used a series of feature preprocessing techniques to remove redundant processing of the API call sequence. Finally, the recurrent neural network method (RNN) was used to build the model and perform detection and verification. Furthermore, this paper constructed a malware detection model based on a two-way recurrent neural network and used the two-way long short-term memory network model (LSTM) to train the data set containing 5986 malware samples and 5065 benign software samples to obtain the final detection model and its parameters. Finally, the feature vector of the APK file to be detected was passed into the model and obtained the detection results. The experimental results indicated that the detection accuracy of this method can reach 93.68%.

Keywords: cloud platform; deep learning; mobile terminal; malware detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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