Risk-Rating Framework for Mobile Applications

With the proliferation of mobile applications, the need for efficient security assessments of these applications has become pivotal. The growth of apps built for various purposes in numerous fields means there are more opportunities for security breaches, creating a demand for an efficient risk-rating system. The role of such a system cannot be overstated in the current age of information when mobile applications hold significant confidential and private data. Existing approaches usually focus on person-to-person risk-rating systems or traditional security methods that are often static and cannot adapt to the rapidly evolving threats. As a result, they often fail to provide accurate or up-to-date assessments, leaving many mobile applications vulnerable to security breaches. In many instances, current methods are incapable of mitigating risks associated with complex operational characteristics of mobile applications and malware.

Technology Description

The technology introduces systems, methods, and computer-readable mediums for training a risk-rating system to assess the risk of a mobile application. Through this technology, features representing operational characteristics of mobile applications and malware are extracted. These extracted features are utilized to train two learning classifiers. The combination of these two classifiers then forms the basis of a machine learning risk-rating model. This model is capable of calculating a risk rating on the basis of the features and a correlation of the features of an application. The approach stands out because it employs different features of mobile applications and malware for training classifiers. It also leverages the combination of two classifiers, offering a more comprehensive risk-rating system. By using operational characteristics and malware features, the technology is able to provide a deep-dive risk assessment suitable for various applications. The system intricately examines the assessed application, making it more effective in identifying potential security risks.