Understanding the Behaviour of Android SMS Malware Attacks With Real Smartphones Dataset

Cybersecurity is an emerging field that has become mandatory to be targeted in all technical aspects. There is exponential growth in the number of Mobile users around the world. People from different communities prefer to buy android mobile phones. On the other hand, cyber attackers or hackers also target these mobile phones for different types of attacks. Android play store has become one of the playgrounds for inviting these types of attacks on android mobile phones. Android SMS malware attack is one of the widely used attacks for stealing and manipulating the data in android mobile phones. This research article presents the exploratory data analysis on android SMS malware attacks on android smartphones for preparing a roadmap for the detection of malware originating through SMS. An application Inside mobile phones that is capable to detect SMS malware attacks can be installed in mobile phones for preventing further access to the file manager of mobile phones. But, the mobile application requires detailed information on the behaviour of SMS malware attacks. The detection model can be formed using deep learning-based approaches for android SMS malware attacks, but it is mandated to clearly identify the attributes and features that contribute to the SMS malware android attack. In this experimental study, we have taken the SMS malware dataset created by injecting SMS malware in real smartphones by the CIC-AndMal2017 dataset. We only focused on android SMS malware attacks from this dataset and performed a detailed analysis to form the main attributes affecting the behaviour of different android SMS malware attacks. The results drawn from this experimental study are helpful for the research community to build deep learning or machine learning-based mobile applications for the successful detection and prevention of such attacks on mobile phones.