Robust clustering methods for detecting smartphone's abnormal behavior

Smartphones have become increasingly popular, and, nowadays, thanks to the use of 3G networks, the need for connectivity in a business environment is significant. Smartphones provide access to a tremendous amount of sensitive information related to business, such as customer contacts, financial data and Intranet networks. If any of this information were to fall into the hands of hackers, it would be devastating for the company. In this paper, we propose a cluster-based approach to detecting abnormal behaviour in smartphone applications. First we carry out various robust clustering techniques that help to identify and regroup applications that exhibit similar behaviour. The clustering results are then used to define a cluster-based outlier factor for each application, which in turn identifies the top n malware applications. Initial results of the experiments prove the efficiency and accuracy of cluster-based approaches in detecting abnormal smartphone applications and those with a low false-alert rate.