Article Preview
TopIn this section, authors are going to present some related works dealing with security in mobile applications.
An android application allowing friends, colleagues, and associates to safely and securely chat together, named “SafeChat”, is presented by (Felter et al., 2020). Security is provided through the use of a standard encryption algorithm.
In order to overcome the problem of permissions on Android, the authors of (Feichtner and Gruber, 2020) propose an approach, based on machine learning, to identify the critical gaps between the behavior of the application described by the developer and the use of permissions through combining the advanced techniques in natural language processing, the deep learning and convolutional neural network.
Another approach for solving the authorization problem in Android and detecting malware is presented in (Olukoya et al., 2019), through the correspondence between the authorization requested by an application and the natural language description of the application.
In order to protect the phone and detect unauthorized or illegitimate users, the authors of (Chandrasekara et al., 2020) suggest an approach for monitoring the user’s behavior through studying a few parameters namely: keystroke dynamics, location detection, voice recognition, and the application usage.
The work presented in (Alkhattabi et al., 2020) aims to analyze the security, the privacy, and the confidentiality of Family Locator applications, which often collect a lot of sensitive information such as: the users’ location and contacts. For doing this, they analyze the permissions requested by 41 FL apps, available on the Google Play Store, in order to understand the type of the collected sensitive information and analyze the network traffic and the local storage of these applications to identify the potential sensitive information leaks.