Open Access
MATEC Web Conf.
Volume 309, 2020
2019 International Conference on Computer Science Communication and Network Security (CSCNS2019)
Article Number 02002
Number of page(s) 8
Section Network Security and Software Design
Published online 04 March 2020
  1. N. McLaughlin, J. M. del Rincon, B. Kang, S. Yerima, Deep Android Malware Detection. ACM Conference on Data and Application Security and Privacy, 2017. [Google Scholar]
  2. Ming Fan, Jun Liu, Xiapu Luo, Kai Chen, Zhenzhou Tian, Qinghua Zheng, Ting Liu. Android Malware Familial Classification and Representative Sample Selection via Frequent Subgraph Analysis. IEEE Transaction on Information Forensics and Security, 2018, 13(8): 1890–1905. [CrossRef] [Google Scholar]
  3. Jixin Zhang, Zheng Qin, Hui Yin, Lu Ou, Kehuan Zhang, A feature-hybrid malware variants detection using CNN based opcode embedding and BPNN based API embedding, Computers & Security, 2019, 84: 376–392. [CrossRef] [Google Scholar]
  4. Silvio Cesare, Yang Xiang, Wanlei Zhou. Control Flow-Based Malware Variant Detection. IEEE Transactions on Dependable and Secure Computing, 2014, 11(4): 307–317. [CrossRef] [Google Scholar]
  5. Jixin Zhang, Zheng Qin, Kehuan Zhang, Hui Yin, Jingfu Zou, Dalvik Opcode Graph based Android Malware Variants Detection, IEEE Access, 2018, 2018, 6: 51964–51974. [CrossRef] [Google Scholar]
  6. Jianjun Huang, Xiangyu Zhang, Lin Tan. AsDroid detecting stealthy behaviors in Android applications by user interface and program behavior contradiction. ACM/IEEE International Conference on Software Engineering, 2014: 1036–1046. [Google Scholar]
  7. Canzanese R. et al. System call-based detection of malicious processes. In proc. of IEEE international conference on software quality, Reliability and Security, 2015: 119–24. [Google Scholar]
  8. Wei Yang, Xusheng Xiao, Benjamin Andow. AppContext difffferentiating malicious and benign mobile app behaviors using context. ACM/IEEE International Conference on Software Engineering, 2015: 303–313. [Google Scholar]
  9. Asaf Shabtai, Yuval Elovici, Uri Kanonov, Yael Weiss, Chanan Glezer. Andromaly: a behavioral malware detection framework for android devices. Journal of Intelligent Information Systems, 2012, 38(1): 161–190. [CrossRef] [Google Scholar]
  10. Konrad R et al Automatic analysis of malware behavior using machine learning. J Comput Secur 2013, 19:639–668. [Google Scholar]
  11. ASPack,", 2019. [Google Scholar]
  12. UPX,, 2019. [Google Scholar]
  13. VMProtect,, 2019. [Google Scholar]
  14. ZProtect,, 2019. [Google Scholar]
  15. VXHeaven,, 2019. [Google Scholar]

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