참고문헌
- Kaspersky. IT threat evolution Q1 2020. Statistics. Accessed: Nov. 19, 2020. [Online]. Available: https://securelist.com/it-threat-evolution-q1-2020-statistics/96959/
- Buchanan, W. J., Chiale, S., Macfarlane, R.: A methodology for the security evaluation within third-party Android Marketplaces. Digital Investigation, 23, 88-98(2017). https://doi.org/10.1016/j.diin.2017.10.002
- Dini, G., Martinelli, F., Matteucci, I., Petrocchi, M., Saracino, A., Sgandurra, D.: Risk analysis of Android applications: A user-centric solution. Future Generation Computer Systems, 80, 505-518(2018). https://doi.org/10.1016/j.future.2016.05.035
- Abdullah, T., Ali, W., Abdulghafor, R.: Empirical Study on Intelligent Android Malware Detection based on Supervised Machine Learning. International Journal of Advanced Computer Science and Applications (IJACSA), 11(4), 215-224(2020).
- Wang, W., Li, Y., Wang, X., Liu, J., Zhang, X.: Detecting Android malicious apps and categorizing benign apps with ensemble of classifiers. Future generation computer systems, 78, 987-994(2018). https://doi.org/10.1016/j.future.2017.01.019
- Idrees, F., Rajarajan, M., Conti, M., Chen, T. M., Rahulamathavan, Y.: PIndroid: A novel Android malware detection system using ensemble learning methods. Computers & Security, 68, 36-46 (2017). https://doi.org/10.1016/j.cose.2017.03.011
- Yerima, S. Y., Sezer, S., McWilliams, G.: Analysis of Bayesian classification-based approaches for Android malware detection. IET Information Security, 8(1), 25-36(2014). https://doi.org/10.1049/iet-ifs.2013.0095
- Yu, H., Xie, T., Paszczynski, S., Wilamowski, B. M.: Advantages of radial basis function networks for dynamic system design. IEEE Transactions on Industrial Electronics, 58(12), 5438-5450(2011). https://doi.org/10.1109/TIE.2011.2164773
- Sharma, A., Dash, S. K.: Mining API calls and permissions for Android malware detection. In Cryptology and Network Security. Cham, Switzerland: Springer Int., pp. 191-205(2014).
- Chan, P. P., Song, W. K.: Static detection of Android malware by using permissions and API calls. In Proc. Int. Conf. Mach. Learn. Cybern., Lanzhou, pp. 82-87(2014).
- Wang, W., Wang, X., Feng, D., Liu, J., Han, Z., Zhang, X.: Exploring permission-induced risk in android applications for malicious application detection. IEEE Transactions on Information Forensics and Security, 9(11), 1869-1882(2014). https://doi.org/10.1109/TIFS.2014.2353996
- Cen, L., Gates, C. S., Si, L., Li, N.: A probabilistic discriminative model for android malware detection with decompiled source code. IEEE Transactions on Dependable and Secure Computing, 12(4), 400-412(2014). https://doi.org/10.1109/TDSC.2014.2355839
- Abdulla, S., Altaher, A.: Intelligent Approach for Android Malware Detection. KSII Transactions on Internet and Information Systems, 9(8): 2964 - 2983(2015). https://doi.org/10.3837/tiis.2015.08.012
- Yuan, Z., Lu, Y., Xue, Y.: Droiddetector: android malware characterization and detection using deep learning. Tsinghua Science and Technology, 21(1), 114-123 (2016). https://doi.org/10.1109/TST.2016.7399288
- Altaher, A.: An improved Android malware detection scheme based on an evolving hybrid neuro-fuzzy classifier (EHNFC) and permission-based features. Neural Computing and Applications, 28(12), 4147-4157(2017). https://doi.org/10.1007/s00521-016-2708-7
- Varsha, M. V., Vinod, P., & Dhanya, K. A.: Identification of malicious android app using manifest and opcode features. Journal of Computer Virology and Hacking Techniques, 13(2), 125-138(2017). https://doi.org/10.1007/s11416-016-0277-z
- Ali, W.: Hybrid Intelligent Android Malware Detection Using Evolving Support Vector Machine ased on Genetic Algorithm and Particle Swarm Optimization. International Journal of Computer Science and Network Security (IJCSNS), 19(9), 15-28 (2019).
- Genome. Android Malware Genome Project. Accessed: February. 14, 2021. [Online]. Available: http://www.malgenomeproject.org
- Contagio. Contagio Mobile: mobile malware mini dump. Accessed: February. 14, 2021. [Online]. Available: http://contagiominidump.blogspot.co.uk
- GitHub. certtools. Accessed: Nov. 20, 2020. [Online]. Available: https://github.com/certtools/malware_name_mapping
- Google Play. Google Play Store. Accessed: Nov. 20, 2020. [Online]. Available: https://play.google.com/store?hl=en
- VirusShare. VirusShare.com. Accessed: Nov. 20, 2020. [Online]. Available: https://virusshare.com
- GitHub. Mitchellkrogza. Accessed: Nov. 20, 2020. [Online]. Available:https://github.com/mitchellkrogza/TheBig-List-of-Hacked-Malware-Web-Sites
- TheZoo. The Zoo aka Malware DB. Accessed: Nov. 20, 2020. [Online]. Available: http://ytisf.github.io/theZoo
- Virusbay. Virusbay.com. Accessed: Nov. 20, 2020. [Online]. Available: https://beta.virusbay.io/
- Dasmalwerk. DAS MALWERK // malware samples. Accessed: Nov. 20, 2020. [Online]. Available: https://dasmalwerk.eu/
- Figshare. Android malware dataset for machine learning 1. Accessed: Nov. 19, 2020. [Online]. Available: https://figshare.com/articles/Android_malware_dataset_for_machine_learning_1/5854590/1
- Yerima, S. Y., & Sezer, S.: Droidfusion: A novel multilevel classifier fusion approach for android malware detection. IEEE transactions on cybernetics, 49(2), 453-466(2018). https://doi.org/10.1109/tcyb.2017.2777960
- Ali, W.: Phishing Website Detection based on Supervised Machine Learning with Wrapper Features Selection. International Journal of Advanced Computer Science and Applications (IJACSA), 8(9), 72-78(2017).
- Ali, W., & Ahmed, A. A.: Hybrid intelligent phishing website prediction using deep neural networks with genetic algorithm-based feature selection and weighting. IET Information Security, 13(6), 659-669(2019). https://doi.org/10.1049/iet-ifs.2019.0006
- Yerima, S. Y., Sezer, S., Muttik, I. High accuracy android malware detection using ensemble learning. IET Information Security, 9(6), 313-320(2015). https://doi.org/10.1049/iet-ifs.2014.0099