參考文獻 |
[ 1 ] ”2018 Malware Forecast: the onward march of Android malware”. (Accessed : 20-May-2018)取自: https://nakedsecurity.sophos.com/2017/11/07/2018-malware-forecast-the-onward-march-of-android-malware/。
[ 2 ] Android Developer : “Define a Custom App Permission”. 2018年4月17日(Accessed : 14-Jun-2018)取自: https://developer.android.com/guide/topics/permissions/defining。
[ 3 ] Android Developer : ”Permissions overview”. 2018年6月15日(Accessed : 14-Jun-2018)取自: https://developer.android.com/guide/topics/permissions/overview。
[ 4 ] Android Developer : “Set up Android Emulator Networking”. 2018年6月5日(Accessed : 26-Jun-2018)取自: https://developer.android.com/studio/run/emulator-networking。
[ 5 ] ”Contagio Malware dump”. (Accessed : 1-Mar-2018)取自: http://contagiodump.blogspot.com/。
[ 6 ] ”Forget The Sheeple: Android fans are atually the most loyal.”. (Accessed : 20-Jun-2018)取自: http://bgr.com/2018/03/08/iphone-vs-android-market-share/。
[ 7 ] “Cisco visual networking index: Global mobile data traf?c forecast update(2017)”. (Accessed : 20-Jun-2018)取自: https://goo.gl/ylTuVx。
[ 8 ] “Global mobile OS market share in sales to end users from 1st quarter 2009 to 2nd quarter 2017”. (Accessed : 20-Jun-2018)取自: https://www.statista.com/statistics/266136/global-market-share-held-by-smartphone-operating-systems/。
[ 9 ] ”Google Play Apps”. (Accessed : 27-May-2018)取自: https://play.google.com/store/apps?hl=zh_TW。
[ 10 ]”Little418:Check APK Permissions with aapt". 2014年7月1日(Accessed : 20-Mar-2018)取自: https://little418.com/2014/07/check-apk-permissions-with-aapt.html。
[ 11 ]”Scapy-Packet crafting for Python2 and Python3”. (Accessed : 20-Fab-2018)取自: https://scapy.net/。
[ 12 ]“WEKA – Performing Attribute Selection”. (Accessed : 1-Jul-2018)取自: https://weka.wikispaces.com/Performing+attribute+selection。
[ 13 ]胡哲君. ”去可識別個人資訊後之 Android 惡意程式動態分析研究; Dynamic Android Malware Analysis with de-identification of personal identifiable information”. 國立中央大學資訊管理學系碩士論文(2017).
[ 14 ]Afonso, V. M., de Amorim, M. F., Gregio, A. R. A., Junquera, G. B., & de Geus, P. L. (2015). Identifying Android malware using dynamically obtained features. Journal of Computer Virology and Hacking Techniques, 11(1), 9-17.
[ 15 ]Aresu, M., Ariu, D., Ahmadi, M., Maiorca, D., & Giacinto, G. (2015, October). Clustering android malware families by http traffic. In Malicious and Unwanted Software (MALWARE), 2015 10th International Conference on (pp. 128-135). IEEE.
[ 16 ]Arp, D., Spreitzenbarth, M., Hubner, M., Gascon, H., Rieck, K., & Siemens, C. E. R. T. (2014, February). DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket. In The Network and Distributed System Security Symposium (NDSS) (Vol. 14, pp. 23-26).
[ 17 ]Bierma, M., Gustafson, E., Erickson, J., Fritz, D., & Choe, Y. R. (2014). Andlantis: Large-scale Android dynamic analysis. arXiv preprint arXiv:1410.7751.
[ 18 ]Blokhin, K., Saxe, J., & Mentis, D. (2013, July). Malware similarity identification using call graph based system call subsequence features. In 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops (pp. 6-10). IEEE.
[ 19 ]Chen, Z., Han, H., Yan, Q., Yang, B., Peng, L., Zhang, L., & Li, J. (2015, August). A first look at android malware traffic in first few minutes. In Trustcom/BigDataSE/ISPA, 2015 IEEE (Vol. 1, pp. 206-213). IEEE.
[ 20 ]Crammer, K., Kulesza, A., & Dredze, M. (2009). Adaptive regularization of weight vectors. In Advances in neural information processing systems (pp. 414-422).
[ 21 ]De la Puerta, J. G., Sanz, B., Grueiro, I. S., & Bringas, P. G. (2015). The Evolution of Permission as Feature for Android Malware Detection. In International Joint Conference (pp. 389-400). Springer, Cham.
[ 22 ]Faruki, P., Ganmoor, V., Laxmi, V., Gaur, M. S., & Bharmal, A. (2013, November). AndroSimilar: robust statistical feature signature for Android malware detection. In Proceedings of the 6th International Conference on Security of Information and Networks (pp. 152-159). ACM.
[ 23 ]Ghaffari, F., Abadi, M., & Tajoddin, A. (2017, May). AMD-EC: Anomaly-based Android malware detection using ensemble classifiers. In Electrical Engineering (ICEE), 2017 Iranian Conference on (pp. 2247-2252). IEEE.
[ 24 ]Hawkins, D. M. (2004). The problem of overfitting. Journal of chemical information and computer sciences, 44(1), 1-12.
[ 25 ]Kandukuru, S., & Sharma, R. M. (2017, April). Android malicious application detection using permission vector and network traffic analysis. In Convergence in Technology (I2CT), 2017 2nd International Conference for (pp. 1126-1132). IEEE.
[ 26 ]Li, D., Wang, Z., Li, L., Wang, Z., Wang, Y., & Xue, Y. (2017, June). FgDetector: Fine-Grained Android Malware Detection. In Data Science in Cyberspace (DSC), 2017 IEEE Second International Conference on (pp. 311-318). IEEE.
[ 27 ]Li, Z., Sun, L., Yan, Q., Srisa-an, W., & Chen, Z. (2016, October). Droidclassifier: Efficient adaptive mining of application-layer header for classifying android malware. In International Conference on Security and Privacy in Communication Systems(pp. 597-616). Springer, Cham.
[ 28 ]Lin, Y. D., Lai, Y. C., Chen, C. H., & Tsai, H. C. (2013). Identifying android malicious repackaged applications by thread-grained system call sequences. computers & security, 39, 340-350.
[ 29 ]Lin, Z., Wang, R., Jia, X., Zhang, S., & Wu, C. (2016, August). Classifying Android malware with dynamic behavior dependency graphs. In Trustcom/BigDataSE/I SPA, 2016 IEEE (pp. 378-385). IEEE.
[ 30 ]Liu, X., & Liu, J. (2014, April). A two-layered permission-based Android malware detection scheme. In Mobile cloud computing, services, and engineering (mobilecloud), 2014 2nd ieee international conference on (pp. 142-148). IEEE.
[ 31 ]Malik, J., & Kaushal, R. (2016, July). CREDROID: Android malware detection by network traffic analysis. In Proceedings of the 1st ACM Workshop on Privacy-Aware Mobile Computing (pp. 28-36). ACM.
[ 32 ]Martin, A., Calleja, A., Menendez, H. D., Tapiador, J., & Camacho, D. (2016, December). ADROIT: Android malware detection using meta-information. In Computational Intelligence (SSCI), 2016 IEEE Symposium Series on (pp. 1-8). IEEE.
[ 33 ]Narayanan, A., Yang, L., Chen, L., & Jinliang, L. (2016, July). Adaptive and scalable android malware detection through online learning. In Neural Networks (IJCNN), 2016 International Joint Conference on (pp. 2484-2491). IEEE.
[ 34 ]Narudin, F. A., Feizollah, A., Anuar, N. B., & Gani, A. (2016). Evaluation of machine learning classifiers for mobile malware detection. Soft Computing, 20(1), 343-357.
[ 35 ]Pang, Y., Chen, Z., Li, X., Wang, S., Zhao, C., Wang, L, & Li, Z. (2017, July). Finding Android Malware Trace from Highly Imbalanced Network Traffic. In Computational Science and Engineering (CSE) and Embedded and Ubiquitous Computing (EUC), 2017 IEEE International Conference on (Vol. 1, pp. 588-595). IEEE.
[ 36 ]Qiao, M., Sung, A. H., & Liu, Q. (2016, July). Merging permission and api features for android malware detection. In 2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) (pp. 566-571). IEEE.
[ 37 ]Sanz, B., Santos, I., Laorden, C., Ugarte-Pedrero, X., Bringas, P. G., & Alvarez, G. (2013). Puma: Permission usage to detect malware in android. In International Joint Conference CISIS’12-ICEUTE 12-SOCO 12 Special Sessions (pp. 289-298). Springer, Berlin, Heidelberg.
[ 38 ]?ah?n, D. O., Kural, O. E., Akleylek, S., & Kilic, E. (2018, March). New results on permission based static analysis for Android malware. In Digital Forensic and Security (ISDFS), 2018 6th International Symposium on (pp. 1-4). IEEE.
[ 39 ]Wang, S., Yan, Q., Chen, Z., Yang, B., Zhao, C., & Conti, M. (2018). Detecting android malware leveraging text semantics of network flows. IEEE Transactions on Information Forensics and Security, 13(5), 1096-1109.
[ 40 ]Wang, S., Yan, Q., Chen, Z., Yang, B., Zhao, C., & Conti, M. (2017, May). TextDroid: Semantics-based detection of mobile malware using network flows. In Computer Communications Workshops (INFOCOM WKSHPS), 2017 IEEE Conference on(pp. 18-23). IEEE.
[ 41 ]Wang, S., Chen, Z., Zhang, L., Yan, Q., Yang, B., Peng, L., & Jia, Z. (2016, June). TrafficAV: An effective and explainable detection of mobile malware behavior using network traffic. In Quality of Service (IWQoS), 2016 IEEE/ACM 24th International Symposium on (pp. 1-6). IEEE.
[ 42 ]Wu, D. J., Mao, C. H., Wei, T. E., Lee, H. M., & Wu, K. P. (2012, August). Droidmat: Android malware detection through manifest and api calls tracing. In Information Security (Asia JCIS), 2012 Seventh Asia Joint Conference on (pp. 62-69). IEEE.
[ 43 ]Xiao, X., Xiao, X., Jiang, Y., Liu, X., & Ye, R. (2016). Identifying Android malware with system call co?occurrence matrices. Transactions on Emerging Telecommunications Technologies, 27(5), 675-684.
[ 44 ]Xu, K., Li, Y., & Deng, R. H. (2016). ICCDetector: ICC-based malware detection on Android. IEEE Transactions on Information Forensics and Security, 11(6), 1252-1264. |