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Using probabilistic generative models for ranking risks of android apps (2012)
| Venue: | In ACM CCS |
| Citations: | 37 - 0 self |
BibTeX
@INPROCEEDINGS{Peng12usingprobabilistic,
author = {Hao Peng and Chris Gates and Bhaskar Sarma and Ninghui Li and Alan Qi and Rahul Potharaju and Cristina Nita-rotaru and Ian Molloy},
title = {Using probabilistic generative models for ranking risks of android apps},
booktitle = {In ACM CCS},
year = {2012},
publisher = {Redacted}
}
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Abstract
One of Android’s main defense mechanisms against malicious apps is a risk communication mechanism which, before a user installs an app, warns the user about the permissions the app requires, trusting that the user will make the right decision. This approach has been shown to be ineffective as it presents the risk information of each app in a “stand-alone ” fashion and in a way that requires too much technical knowledge and time to distill useful information. We introduce the notion of risk scoring and risk ranking for Android apps, to improve risk communication for Android apps, and identify three desiderata for an effective risk scoring scheme. We propose to use probabilistic generative models for risk scor-ing schemes, and identify several such models, ranging from the simple Naive Bayes, to advanced hierarchical mixture models. Ex-perimental results conducted using real-world datasets show that probabilistic general models significantly outperform existing ap-proaches, and that Naive Bayes models give a promising risk scor-ing approach.
Keyphrases
android apps probabilistic generative model right decision malicious apps naive bayes model stand-alone fashion probabilistic general model real-world datasets effective risk risk communication risk scoring risk ranking useful information ex-perimental result risk scor-ing scheme promising risk scor-ing approach risk communication mechanism simple naive bayes several model android main defense mechanism hierarchical mixture model risk information much technical knowledge
