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With the proliferation of Android malware, the demand for an effective and efficient malware detection system is on the rise.
2011
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Y. Zhou and X. Jiang, “Dissecting Android malware: Characterization and evolution,” in S&P , 2012
2012
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Z. Aung and W. Zaw, “Permission-based Android malware detection,” International Journal of Scientific & Technology Research , 2013
2013
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D. Arp, M. Spreitzenbarth, M. Hubner, H. Gascon, K. Rieck, and C. Siemens, “Drebin: Effective and explainable detection of Android malware in your pocket.” in NDSS , 2014
2014
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Q. Jerome, K. Allix, R. State, and T. Engel, “Using opcode-sequences to detect malicious Android applications,” in 2014 , ICC
2014
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P. Rovelli and Y. Vigfusson, “PMDS: Permission-based malware detection system,” in ICISS , 2014
2014
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H. K. et al., “Detecting and classifying Android malware using static analysis along with creator information,” International Journal of Distributed Sensor Networks , 2015
2015
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S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” ser. ICML, 2015
2015
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K. A. Talha, D. I. Alper, and C. Aydin, “Apk Auditor: Permission-based Android malware detection system,” Digital Investigation , 2015
2015
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S. Chen, M. Xue, Z. Tang, L. Xu, and H. Zhu, “Stormdroid: A streaminglized machine learning-based system for detecting Android malware,” in AsiaCCS , 2016
2016
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S. Verma and S. Muttoo, “An Android malware detection framework-based on permissions and intents,” Defence Science Journal , 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
S. Chen, M. Xue, and L. Xu, “Towards adversarial detection of mobile malware,” in MobiCom , 2016
2016
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L. Fan, M. Xue, S. Chen, L. Xu, and H. Zhu, “Accuracy vs. time cost: Detecting Android malware through pareto ensemble pruning,” in CCS , 2016
2016
Cited alongside, same era.
N. McLaughlin, J. Martinez del Rincon, B. Kang, S. Yerima, P. Miller, S. Sezer, Y. Safaei, E. Trickel, Z. Zhao, A. Doupé et al. , “Deep android malware detection,” in CODASPY , 2017
2017
S. Chen, T. Su, L. Fan, G. Meng, M. Xue, Y. Liu, and L. Xu, “Are mobile banking apps secure? what can be improved?” in FSE , 2018
2018
Later among the works it cites.
S. Chen, M. Xue, L. Fan, S. Hao, L. Xu, H. Zhu, and B. Li, “Automated poisoning attacks and defenses in malware detection systems: An adversarial machine learning approach,” Computers & Security , 2018
2018
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Z. Xu, K. Ren, S. Qin, F. Craciun, J. Sun, and M. Sun, “Cdgdroid: Android malware detection based on deep learning using CFG and DFG,” in Formal Methods and Software Engineering , 2018
2018
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C. Tang, S. Chen, L. Fan, L. Xu, Y. Liu, Z. Tang, and L. Dou, “A large-scale empirical study on industrial fake apps,” in ICSE , 2019
2019
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R. Feng, S. Chen, X. Xie, L. Ma, G. Meng, Y. Liu, and S.-W. Lin, “MobiDroid: A performance-sensitive malware detection system on mobile platform,” in ICECCS , 2019
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Cited alongside, same era.
R. Nix and J. Zhang, “Classification of Android apps and malware using deep neural networks,” in 2017 , IJCNN
2017
Cited alongside, same era.
A. Feizollah, N. Anuar, R. Salleh, G. Suarez-Tangil, and S. Furnell, “Androdialysis: Analysis of Android intent effectiveness in malware detection,” Computers & Security , vol. 65, 2017
2017
Cited alongside, same era.
R. Vinayakumar, K. P. Soman, and P. Poornachandran, “Deep Android malware detection and classification,” in ICACCI , 2017
2017
Cited alongside, same era.
X. Xiao, S. Zhang, F. Mercaldo, G. Hu, and A. K. Sangaiah, “Android malware detection based on system call sequences and LSTM,” Multimedia Tools and Applications , 2017
2017
Cited alongside, same era.
S. Alam, Z. Qu, R. Riley, Y. Chen, and V. Rastogi, “Droidnative: Automating and optimizing detection of Android native code malware variants,” Computers & Security , 2017
2017
Cited alongside, same era.
Java native interface (JNI) framework. [Online]. Available: https://docs.oracle.com/javase/7/docs/technotes/guides/jni/spec/jniTOC.html
Cited in the paper.
Tensorflow. [Online]. Available: https://www.tensorflow.org/
Cited in the paper.
2019
Later among the works it cites.
Z. Ma, H. Ge, Y. Liu, M. Zhao, and J. Ma, “A combination method for Android malware detection based on control flow graphs and machine learning algorithms,” IEEE Access , 2019
2019
Later among the works it cites.
J. Yan, G. Yan, and D. Jin, “Classifying malware represented as control flow graphs using deep graph convolutional neural network,” DSN , 2019
2019
Later among the works it cites.
S. Chen, L. Fan, G. Meng, T. Su, M. Xue, Y. Xue, Y. Liu, and L. Xu, “An empirical assessment of security risks of global android banking apps,” in ICSE , 2020
2020
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