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[1] Android sdk. https://developer.android.com/sdk/installing/ adding-packages.html. [2] Android secure tips. http://developer.android.com/training/articles/ security-tips.html. [3] Android test automation framework ”robotium”. https://code.google.com/ p/robotium/. [4] android tool. https://developer.android.com/tools/help/android.html. [5] Android tool ”monkey”. http://developer.android.com/tools/help/ monkey.html. [6] Android tool ”monkeyrunner”. https://developer.android.com/tools/ help/monkeyrunner_concepts.html. [7] Arff format. http://www.cs.waikato.ac.nz/ml/weka/arff.html. [8] Cross validation. https://cg2010studio.wordpress.com/2012/10/22/%E4% BA%A4%E5%8F%89%E9%A9%97%E8%AD%89-cross-validation/. [9] Gray box testing. http://en.wikipedia.org/wiki/Gray_box_testing. [10] Jarsigner. http://docs.oracle.com/javase/6/docs/technotes/tools/ windows/jarsigner.html. [11] Keytool. http://docs.oracle.com/javase/7/docs/technotes/tools/ solaris/keytool.html. [12] Kstar algorithm. http://weka.sourceforge.net/doc.dev/weka/ classifiers/lazy/KStar.html. [13] Official android blog. http://googlemobile.blogspot.tw/2012/02/ android-and-security.html. [14] Part algorithm. http://wiki.pentaho.com/display/DATAMINING/PART. [15] Randomcommittee algorithm. http://wiki.pentaho.com/display/DATAMINING/RandomCommittee. [16] virustotal. https://www.virustotal.com/zh-tw/. [17] Weka library. http://www.cs.waikato.ac.nz/ml/weka/. [18] Smartphone os market share. IDC, Q3, 2014. http://www.idc.com/prodserv/ smartphone-os-market-share.jsp. [19] Mobile threat report. F-Secure, Q4, 2012. [20] Y. Elovici C. Glezer A. Shabtai, U. Kanonov and Y. Weiss. “andromaly”: a behavioral malware detection framework for android devices. In Journal of Intelligent Information Systems, pages 161–190, 2012. [21] A.P. Fuchs, A. Chaudhuri, and J.S. Foster. SCanDroid Automated Security Certification of Android Applications. Technical Report CSTR-4991, Department of Computer Science, University of Maryland, 2009. [22] H. S. Ham and M.J. Choi. Analysis of android malware detection performance using machine learning classifiers. In Proceedings of International Conference on ICT Convergence (ICTC), pages 490–495, 2013. [23] M. Karami, M.and Elsabagh, P. Najafiborazjani, and A. Stavrou. Behavioral analysis of android applications using automated instrumentation. In Proceedings of IEEE 7th International Conference on Software Security and Reliability- Companion, pages 182–187, 2013. [24] H. Kim, J. Smith, and K. G. Shin. Detecting energy-greedy anomalies and mobile malware variants. In Proceedings of the 6th international conference on Mobile systems, applications, and services, pages 239––252. ACM, 2008. [25] P. Lantz. An android application sandbox for dynamic analysis. Master’s thesis, lectrical and Information Technology, Lund university. [26] A. Mujumda, G. Masiwal, and Dr. B.B. Meshram. Analysis of signature-based and behavior-based anti-malware approaches. In Proceedings of International Journal of Advanced Research in Computer Engineering and Technology, 2013. [27] Jon Oberheide and Charlie Miller. Dissecting the android bouncer. BUSTICATI PRODUCTIONS PRESENTS, 2012. [28] Szor. The Art of Computer Virus Research and Defense. Addison-Wesley, 2005. [29] Li-Luen Tsai. An android machine learning malware detection system using the result of static analysis and dynamic analysis as the features. Master’s thesis, National Chiao Tung University, 2014. [30] M. Ongtang W. Enck and P. McDaniel. On lightweight mobile phone application certification. In Proceedings of the 16th ACM conference on Computer and communications security, pages 235–245. ACM, 2009. [31] M. Zhao, F. Ge, T. Zhang, and Z. Yuan. Antimaldroid: An efficient svmbased malware detection framework for android. Springer, 2011. [32] C. Zheng, S. Zhu, S. Dai, G. Gu, X. Gong, X. Han, and W. Zou. Smartdroid: an automatic system for revealing ui-based trigger conditions in android applications. In Proceedings of the second ACM workshop on Security and privacy in smartphones and mobile devices, pages 93–104, 2012. |