Pwc. (2019). Global Consumer Insights Survey Available: https://www.pwc.com/gx/en/industries/consumer-markets/consumer-insights-survey.html
 Gartner. (2018, 10-Jun). Gartner Says Huawei Secured No. 2 Worldwide Smartphone Vendor Spot, Surpassing Apple in Second Quarter 2018. Available: https://www.gartner.com/en/newsroom/press-releases/2018-08-28-gartner-says-huawei-secured-no-2-worldwide-smartphone-vendor-spot-surpassing-apple-in-second-quarter
 McAfee. (2019). McAfee Labs Threats Report December 2018. Available: https://www.mcafee.com/enterprise/en-us/assets/reports/rp-quarterly-threats-dec-2018.pdf
 Wiki. Static program analysis. Available: https://en.wikipedia.org/wiki/Static_program_analysis
 Wiki. Dynamic program analysis. Available: https://en.wikipedia.org/wiki/Dynamic_program_analysis
 . Contagio Blog. Available: http://contagiominidump.blogspot.tw/
 . Baidu Apps Market. Available: https://shouji.baidu.com/
 . Android Drebin Project. Available: https://www.sec.cs.tu-bs.de/~danarp/drebin/
 . Apktool(A tool for reverse engineering 3rd party). Available: https://ibotpeaches.github.io/Apktool
 G. Paller. Dalvik opcodes. Available: http://pallergabor.uw.hu/androidblog/dalvik_opcodes.html
 . APKPure. Available: https://apkpure.com/tw/
 . Android Malware Dataset. Available: http://amd.arguslab.org/
 游子慧, "基於靜態特徵與機器學習之 Android 惡意程式分類研究," 國立中央大學資訊管理所碩士論文, 2017.
 胡哲君, "去可識別個人資訊後之 Android惡意程式動態分析研究," 國立中央大學資訊管理所碩士論文, 2017.
 王奕鈞, "Android平台下整合控制流與操作碼之惡意程式分析," 國立中央大學資訊管理所碩士論文, 2018.
 M. Pomilia, "A study on obfuscation techniques for Android malware," ed: Master’s thesis. Sapienza University of Rome, 2016.
 Z. Chen et al., "A first look at android malware traffic in first few minutes," in 2015 IEEE Trustcom/BigDataSE/ISPA, 2015, vol. 1, pp. 206-213: IEEE.
 H. Qi and A. Gani, "Research on mobile cloud computing: Review, trend and perspectives," in 2012 Second International Conference on Digital Information and Communication Technology and it′s Applications (DICTAP), 2012, pp. 195-202: ieee.
 L. Nataraj, S. Karthikeyan, G. Jacob, and B. Manjunath, "Malware images: visualization and automatic classification," in Proceedings of the 8th international symposium on visualization for cyber security, 2011, p. 4: ACM.
 B. Chen, Z. Ren, C. Yu, I. Hussain, and J. J. I. A. Liu, "Adversarial Examples for CNN-Based Malware Detectors," vol. 7, pp. 54360-54371, 2019.
 R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, "Grad-cam: Visual explanations from deep networks via gradient-based localization," in Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 618-626.
 L. Perez and J. J. a. p. a. Wang, "The effectiveness of data augmentation in image classification using deep learning," 2017.
 I. Santos, F. Brezo, X. Ugarte-Pedrero, and P. G. J. I. S. Bringas, "Opcode sequences as representation of executables for data-mining-based unknown malware detection," vol. 231, pp. 64-82, 2013.
 Q. Jerome, K. Allix, R. State, and T. Engel, "Using opcode-sequences to detect malicious Android applications," in 2014 IEEE International Conference on Communications (ICC), 2014, pp. 914-919: IEEE.
 B. Kang, S. Y. Yerima, K. McLaughlin, and S. Sezer, "N-opcode analysis for android malware classification and categorization," in 2016 International Conference On Cyber Security And Protection Of Digital Services (Cyber Security), 2016, pp. 1-7: IEEE.
 N. McLaughlin et al., "Deep Android Malware Detection," presented at the Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy - CODASPY ′17, 2017.
 Y. LeCun, L. Bottou, Y. Bengio, and P. J. P. o. t. I. Haffner, "Gradient-based learning applied to document recognition," vol. 86, no. 11, pp. 2278-2324, 1998.
 M. Yang and Q. Wen, "Detecting android malware by applying classification techniques on images patterns," in 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 2017, pp. 344-347: IEEE.
 J. Yan, Y. Qi, Q. J. S. Rao, and C. Networks, "Detecting malware with an ensemble method based on deep neural network," vol. 2018, 2018.
 T. Hsien-De Huang and H.-Y. Kao, "R2-d2: Color-inspired convolutional neural network (cnn)-based android malware detections," in 2018 IEEE International Conference on Big Data (Big Data), 2018, pp. 2633-2642: IEEE.
 D. Bahdanau, K. Cho, and Y. J. I. A. Bengio, "Neural machine translation by jointly learning to align and translate," 2014.
 I. Sutskever, O. Vinyals, and Q. V. Le, "Sequence to sequence learning with neural networks," in Advances in neural information processing systems, 2014, pp. 3104-3112.
 H. Yakura, S. Shinozaki, R. Nishimura, Y. Oyama, and J. Sakuma, "Malware Analysis of Imaged Binary Samples by Convolutional Neural Network with Attention Mechanism," in Proceedings of the Eighth ACM Conference on Data and Application Security and Privacy, 2018, pp. 127-134: ACM.
 K. Xu et al., "Show, attend and tell: Neural image caption generation with visual attention," in International conference on machine learning, 2015, pp. 2048-2057.
 A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Advances in neural information processing systems, 2012, pp. 1097-1105.
 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, vol. 14, pp. 23-26.
 C. Hasegawa and H. Iyatomi, "One-dimensional convolutional neural networks for Android malware detection," in 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA), 2018, pp. 99-102: IEEE.
 L. Shiqi, T. Shengwei, Y. Long, Y. Jiong, S. J. K. T. o. I. Hua, and I. Systems, "Android malicious code Classification using Deep Belief Network," vol. 12, no. 1, 2018.