Mobile apps are at the center of everyone’s daily lives and users give them access to their intimate personal data. Therefore, it is important to develop methods for figuring out how much an app can detect and collect from its users, and whether that access is in line with their expectations of privacy. Several methods have been devised to determine app intrusiveness, including analysis of their descriptions and conformity with their programmed behavior. Most of the existing approaches depend on static analysis that is not easily done on the go. We propose a novel method to determine whether an app is intrusive based on the app description which can allow users to make decisions before downloading. More specifically, we used a Long Short-Term Memory (LSTM) network to analyze the descriptions, along with a multi-layer perceptron (MLP) network to process hints provided by other app features. This combined network structure achieved 79% and 74% accuracy rates for training and validation, respectively. Our findings indicate that not only it is possible to use the description and other readily available information to predict the intrusiveness of an app, but also that the network required to do the job is fairly small.
|Name||Advances in Intelligent Systems and Computing|
|Conference||The Future Technologies Conference 2020 (FTC 2020) |
|Period||05/11/2020 → 06/11/2020|