A neural network approach to category validation of Android applications

Permission structure of Android applications introduces security vulnerabilities which can be readily exploited by third-party applications. We address certain exploitability aspects by means of neural networks, effective classification techniques capable of verifying the application categories. We devise a novel methodology to verify an application category by machine-learning the application permissions and estimating likelihoods of the extant categories. The performance of our classifier is optimized through the joint minimization of false positive and negative rates. Applying our modus operandi to 1,700 popular third-party Android applications and malwares, a major portion of the category declarations were judged truthfully. This manifests effectiveness of neural network decision engines in validating Android application categories.