Machine learning classifiers for android malware analysis

Android is an operating system which currently has over one billion active users for all their mobile devices, with a market impact that is influencing an increase in the amount of information that can be obtained from different users, facts that have motivated the development of malware by cybercriminals. To solve the problems caused by malware, Android implements a different architecture and security controls, such as unique user ID (UID) for each application, system permissions, and its distribution platform Google Play. It has been shown that there are ways to violate that protection, and how the complexity for create a new solutions are increased while cybercriminals improve their skills to develop malware. The developer and researchers community has been developing alternatives aimed at improving the level of safety, some solutions have been proposed: analysis techniques, frameworks, sandboxes, and systems security. Most solutions have adopted a cloud computing model with different tools and analysis techniques, one of the most promising ways is the implementation of artificial intelligence solutions for malware analysis. This work proposes a new module that implements a static analysis framework with six algorithms of machine learning for detect malware for Android.

[1]  Jared Smith,et al.  A Dataset of Open-Source Android Applications , 2015, 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories.

[2]  Sahin Albayrak,et al.  Using static analysis for automatic assessment and mitigation of unwanted and malicious activities within Android applications , 2011, 2011 6th International Conference on Malicious and Unwanted Software.

[3]  Yajin Zhou,et al.  Dissecting Android Malware: Characterization and Evolution , 2012, 2012 IEEE Symposium on Security and Privacy.

[4]  Jonathon T. Giffin,et al.  Impeding Malware Analysis Using Conditional Code Obfuscation , 2008, NDSS.

[5]  Sakir Sezer,et al.  A New Android Malware Detection Approach Using Bayesian Classification , 2013, 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA).

[6]  Philip K. Chan,et al.  Machine Learning for Computer Security , 2006, J. Mach. Learn. Res..

[7]  Collin Mulliner,et al.  Android Hacker's Handbook , 2014 .

[8]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[9]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[10]  Ali Feizollah,et al.  Evaluation of machine learning classifiers for mobile malware detection , 2014, Soft Computing.

[11]  Xingquan Zhu,et al.  Machine Learning for Android Malware Detection Using Permission and API Calls , 2013, 2013 IEEE 25th International Conference on Tools with Artificial Intelligence.

[12]  Nikolay Elenkov.  Android Security Internals: An In-Depth Guide to Android's Security Architecture , 2014 .

[13]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[14]  Ali Feizollah,et al.  A Study Of Machine Learning Classifiers for Anomaly-Based Mobile Botnet Detection , 2013 .

[15]  Latifur Khan,et al.  A Machine Learning Approach to Android Malware Detection , 2012, 2012 European Intelligence and Security Informatics Conference.

[16]  Sotiris Ioannidis,et al.  Rage against the virtual machine: hindering dynamic analysis of Android malware , 2014, EuroSec '14.

[17]  Yang Chen,et al.  A neural network approach to category validation of Android applications , 2013, 2013 International Conference on Computing, Networking and Communications (ICNC).

[18]  Shih-Hao Hung,et al.  DroidDolphin: a dynamic Android malware detection framework using big data and machine learning , 2014, RACS '14.