Applying machine learning classifiers to dynamic Android malware detection at scale
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| Authors | Brandon Amos, Hamilton Turner, Jules White |
| Journal/Conference Name | 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC) |
| Paper Category | Computer Networks and Communications |
| Paper Abstract | The widespread adoption and contextually sensitive nature of smartphone devices has increased concerns over smartphone malware. Machine learning classifiers are a current method for detecting malicious applications on smartphone systems. This paper presents the evaluation of a number of existing classifiers, using a dataset containing thousands of real (i.e. not synthetic) applications. We also present our STREAM framework, which was developed to enable rapid large-scale validation of mobile malware machine learning classifiers. |
| Date of publication | 2013 |
| Code Programming Language | Shell |
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