Machine learning classifiers for android malware analysis

Machine learning classifiers for android malware analysis

Abstract:

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.

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