An Efficient Method for Analyzing Widget Intent of Android System
Pages 78 - 85
Abstract
With the improvement of the status of mobile phones and other mobile terminals in life, the privacy protection of mobile phones is still a big problem. Aiming at the Android system, the mobile terminal operating system with the largest share today, this paper proposes a widget intention analysis method to solve the problem of how to judge the user's operation intention in privacy protection. This intention analysis method uses MobilenetV3 to extract image information, BiLSTM to extract text information, then constructs joint features together, and uses deep learning technology to realize intention analysis of widgets. Through comparative experiments, our method improves the detection accuracy and reduces the training time for the existing methods.
References
[1]
Android vs. iOS Smartphone OS sales market share evolution from https://www.kantarworldpanel.com/global/smartphone-os-market-share/
[2]
China Mobile Security Report in 2019 from https://zt.360.cn/1101061855.php?dtid=1101061451&did=610435085
[3]
X Chen, S. Zhu, "DroidJust: automated functionality-aware privacy leakage analysis for Android applications[C]", Proceedings of the 8th ACM Conference on Security & Privacy in Wireless and Mobile Networks., pp. 5, 2015.
[4]
Z. Yang, M. Yang, Y. Zhang, G. Gu, P. Ning, X. S. Wang, "Appintent: Analyzing sensitive data transmission in android for privacy leakage detection", Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security CCS’ 13, pp. 1043-1054, 2013.
[5]
X. Xiao, X. Wang, Z. Cao, H. Wang and P. Gao, "IconIntent: Automatic Identification of Sensitive UI Widgets Based on Icon Classification for Android Apps," 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE), Montreal, QC, Canada, 2019, pp. 257-268.
[6]
Xi S, Yang S, Xiao X, DeepIntent: Deep Icon-Behavior Learning for Detecting Intention-Behavior Discrepancy in Mobile Apps[C]//Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2019: 2421-2436.
[7]
Howard, Andrew, “Searching for MobileNetV3.” 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 1314–1324.
[8]
A. Aziz Sharfuddin, M. Nafis Tihami and M. Saiful Islam, "A Deep Recurrent Neural Network with BiLSTM model for Sentiment Classification," 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), Sylhet, 2018, pp. 1-4.
[9]
B. Andow, A. Acharya, D. Li, W. Enck, K. Singh, T. Xie, "UIRef: Analysis of sensitive user inputs in android applications", ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec), 2017.
[10]
Y. Nan, "Identifying User-Input Privacy in Mobile Applications at a Large Scale" in IEEE Transactions on Information Forensics and Security, vol. 12, no. 3, pp. 647-661, March 2017.
[11]
J. Huang, Z. Li, X. Xiao, Z. Wu, K. Lu, X. Zhang, G. Jiang, "SUPOR: precise and scalable sensitive user input detection for android apps", USENIX Security Symposium, 2015.
[12]
Huang, Gao, “Densely Connected Convolutional Networks.” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2261–2269.
[13]
Cho, Kyunghyun, “Learning Phrase Representations Using RNN Encoder–Decoder for Statistical Machine Translation.” Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 1724–1734.
[14]
Jiasen Lu, Jianwei Yang, Dhruv Batra, and Devi Parikh. 2016. Hierarchical question-image co-attention for visual question answering. In Advances In Neural
[15]
M. Lee, J. Lee, J. Kim, B. Kim and J. Kim, "The Sparsity and Activation Analysis of Compressed CNN Networks in a HW CNN Accelerator Model," 2019 International SoC Design Conference (ISOCC), Jeju, Korea (South), 2019, pp. 255-256.
[16]
Howard, Andrew G., “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.” ArXiv Preprint ArXiv:1704.04861, 2017.
[17]
Sandler M, Howard A, Zhu M, MobileNetV2: Inverted Residuals and Linear Bottlenecks[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018.
[18]
M. Tan, B. Chen, R. Pang, V. Vasudevan, and Q. V. Le. Mnasnet: Platform-aware neural architecture search for mobile. CoRR, abs/1807.11626, 2018.
[19]
T. Yang, A. G. Howard, B. Chen, X. Zhang, A. Go, M. Sandler, V. Sze, and H. Adam. Netadapt: Platform-aware neural network adaptation for mobile applications. In ECCV, 2018
[20]
Hochreiter S, Schmidhuber J . Long Short-Term Memory[J]. Neural Computation, 1997, 9(8):1735-1780.
[21]
Li Yang, Yuxi Wu, Junli Wang and Yili Liu. Research on recurrent neural network[J]. Journal of Computer Applications,2018,38(S2):1-6+26.
[22]
Hao Shu, Chen Wang and Yin Shi. Intrusion detetction based on BiLSTM and attention mechanism[J]. Computer Engineering And Design, 2020,41(11):3042-3046.
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Published In
February 2021
342 pages
ISBN:9781450389174
DOI:10.1145/3456415
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Published: 06 June 2021
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ICCBN 2021
ICCBN 2021: 2021 9th International Conference on Communications and Broadband Networking
February 25 - 27, 2021
Shanghai, China
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