Real-Time Threat Assessment through Machine Learning intended for Enhancing Security Measures in Mobile App

Abstract

The ubiquitous presence of mobile operating systems like Android and iOS has fuelled the development of feature-rich applications, accommodating the diverse needs of users, including the management of personal data such as location and contacts. However, the expansive app ecosystem has also become a target for malicious actors seeking to exploit sensitive user information. Current approaches, such as permissions-based security models, provide only partial insights into app behaviors, leaving users vulnerable to privacy breaches.

In response to this challenge, we present Threat Check, an automated framework designed for continuous threat assessment of mobile applications. Unlike conventional methods that rely on user intervention and contextual understanding, threat Check leverages a one-time initialization process, where users specify trusted applications and rank permission groups based on relevance. Subsequently, threat Check dynamically assesses the threat level of installed applications by monitoring their runtime behaviors and interactions with system services, comparing them against established baselines.

Through its real-time threat rankings facilitated by threat Prior, threat Check empowers users to make informed decisions about app safety, providing ongoing insights into application behaviors and potential privacy violations. By automating threat assessment and enhancing user awareness, threat Check offers a proactive solution to mobile application security, enabling users to mitigate threats effectively and protect their personal data in an ever-evolving threat landscape.