Mobile Apps identification based on network flows

Network operators and mobile carriers are facing serious security challenges caused by an increasing number of services provided by smartphone Apps. For example, Android OS has more than 1 million Apps in stores. Hence, network administrators tend to adopt strict policies to secure their infrastructure. The aim of this study is to propose an efficient framework that has a classification component based on traffic analysis of Android Apps. The framework differs from other proposed studies by focusing on identifying Apps traffic from a network perspective without introducing any overhead on subscribers smartphones. Additionally, it involves a technique for pre-processing network flows generated by Apps to acquire a set of features that are used to build an identification model using machine learning algorithms. The classification model is built using classification ensembles. A group of chosen users contribute in training the classification model, which learns the normal behavior of selected Apps. Eventually, the model should be able to detect abnormal behavior of similar Apps across the network. A 93.78% classification accuracy is achieved with a low false positive rate under 0.5%. In addition, the framework is able to detect abnormal flows of unknown classes by implementing an outlier detection mechanism and reported a 94% accuracy.