[1] SonicWall, Inc. 2019 SonicWall cyber threat report [EB/OL]. [2019-08-05]. https://www.sonicwall.com/lp/2019-cyber-threat-repo- rt-lp/
[2] Christodorescu M, Jha S, Seshia S A, et al. Semantics-aware malware detection [C]// S&P'05: Proceedings of the 2005 IEEE Symposium on Security and Privacy. Washington, DC: IEEE, 2005: 32-46.
[3] Markel Z, Bilzor M. Building a machine learning classifier for malware detection [C]// WATeR 2014: 2014 Second Workshop on Anti-malware Testing Research. Washington, DC: IEEE, 2014: 1-4.
[4] 陈泽峰, 方勇, 刘亮, 等. 基于多维特征的 Android 恶意应用检测系统[J]. 信息安全研究, 2018, 4(2): 133-139.
[5] Raff E, Sylvester J, Nicholas C. Learning the pe header, malware detection with minimal domain knowledge [C]// AISec2017: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security. New York, NY: ACM, 2017: 121-132.
[6] Ma Z, Ge H, Liu Y, et al. A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms [J]. IEEE Access, 2019, 7: 21235-21245.
[7] 孙润康, 彭国军, 李晶雯, 等. 基于行为的 Android 恶意软件判定方法及其有效性[J]. 计算机应用, 2016, 36(4): 973-978.
[8] Wang S, Chen Z, Yan Q, et al. A mobile malware detection method using behavior features in network traffic [J]. Journal of Network and Computer Applications, 2019, 133: 15-25.
[9] 张晨斌, 张云春, 郑杨, 等. 基于灰度图纹理指纹的恶意软件分类[J]. 计算机科学, 2018, 45(6A): 383-386.
[10] Cui Z, Xue F, Cai X, et al. Detection of malicious code variants based on deep learning [J]. IEEE Transactions on Industrial Informatics, 2018, 14(7): 3187-3196.
[11] Shalaginov A, Banin S, Dehghantanha A, et al. Machine learning aided static malware analysis: A survey and tutorial [M]// Cyber Threat Intelligence. Cham: Springer, 2018: 7-45.
[12] Zhao J, Zhang S, Liu B, et al. Malware Detection Using Machine Learning Based on the Combination of Dynamic and Static Features [C]// ICCCN 2018: 2018 27th International Conference on Computer Communication and Network. Washington, DC: IEEE, 2018: 1-6.
[13] Damodaran A, Di T F, Visaggio C A, et al. A comparison of static, dynamic, and hybrid analysis for malware detection [J]. Journal of Computer Virology and Hacking Techniques, 2017, 13(1): 1-12.
[14] 苏志达,祝跃飞,刘龙.基于深度学习的安卓恶意应用检测[J].计算机应用, 2017, 37(6): 1650-1656.
[15] Liu L, Wang B. Automatic malware detection using deep learning based on static analysis [C]// ICPCSEE 2017: International Conference of Pioneering Computer Scientists, Engineers and Educators 2017. Berlin, German: Springer, 2017: 500-507.
[16] Karbab E M B, Debbabi M, Derhab A, et al. MalDozer: automatic framework for android malware detection using deep learning [J]. Digital Investigation, 2018, 24: S48-S59.
[17] Massarelli L, Di Luna G A, Petroni F, et al. Safe: Self-attentive function embeddings for binary similarity [C]// DIMVA2019: International Conference on Detection of Intrusions and Malware & Vulnerability Assessment. Berlin, German: Springer, 2019: 309-329.
[18] Redmond K, Luo L, Zeng Q. A cross-architecture instruction embedding model for natural language processing-inspired binary code analysis [J]. arXiv preprint arXiv:1812.09652, 2018.
[19] Bouvrie, Jake. "Notes on convolutional neural networks." (2006).
[20] Hochreiter S, Schmidhuber J. Long short-term memory [J]. Neural computation, 1997, 9(8): 1735-1780.
[21] Mikolov T, Le Q V, Sutskever I. Exploiting similarities among languages for machine translation [J]. arXiv preprint arXiv:1309.4168, 2013.
[22] Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality [C]// NIPS2013: Advances in Neural Information Processing Systems 26. Cambridge, MA: MIT Press, 2013: 3111-3119.
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