… rating when they lack the knowledge/background to assess the risk level of the target apps.
… [6] used probabilistic models to detect malicious apps, based on the requested permissions …

… Peng H (2012) Using probabilistic generative models for ranking risks of android apps. In:
Proceedings of the 2012 ACM Conference on Computer and Communications Security, pp. …

… W, we can automatically label the risk category for the new apps following the same feature
… Using Probabilistic Generative Models for Ranking Risks of Android Apps. In CCS. 241–252…

… Android apps. Peng et al. [15] propose probabilistic generative models to rank risks of Android
apps … , and Hierarchical Mixture of Naive Bayes models. They show that the Naive Bayes …

… of Android apps are changed over the time. Empirical evaluations on recent and previous
malwares and benign apps … I (2012) Using probabilistic generative models for ranking risks of …

… the probabilities that the sentence is positive, negative and neutral as well as a compound
score. The probability for … 12 popular apps with a numeric star rating above 3.5 and over 1000 …

… IR Apps in different type, we give out a hierachical privacy risk factor set. Then an IR App
privacy security risk evaluation model … , which are selected for its top-ranking in its App type. The …

… or by optimising a generative model iteratively and producing the styled image through a
single … by the number of downloads, number of reviews, and average rating similar to what was …

… store using probabilistic techniques [26]. Cen et al. [27] propose a crowdsourcing ranking
approach to evaluate the risks of applications from … way for evaluating the risks of using apps. …

… techniques, including quantitative risk metrics for ranking sensitive data flows and sinks
in Android apps. … The permission risk values are generated from probabilistic Bayesian-Network …