Paper Info
Title
ARM: ANN-based ranking model for privacy and security analysis in smartphone ecosystems
Abstract
Smartphone ecosystems are considered as a unique source due to the large number of apps which in turn makes an extensive use of personal data. Currently, there is no privacy and security preservation mechanism in smartphone ecosystems to enable users to compare apps in terms of privacy and security protection level, and to alarm them regarding the invasive issues (in terms of privacy and security) of apps before installing them. In this paper, we exploit user comments on app stores as an important source to extract privacy and security invasive (PSI) claims corresponding to apps. Thus, we propose an artificial neural network (ANN)-based ranking model (ARM) in order to classify user comments with privacy and security concerns. Our ranking model is based on three main features namely privacy and security, sentiment, and lifetime analyses as the input of the ranking model along with a novel mathematical formulation in such a way as to maximise the differentiation between comments. The performance results show that ARM is able to classify and predict PSI user comments with accuracy as high as 93.3%. Our findings confirm that due to the functionality of ARM, it has the potential to be widely adopted in smartphone ecosystems.
Year
DOI
Venue
2017
10.1109/CCST.2017.8167854
2017 International Carnahan Conference on Security Technology (ICCST)
Keywords
Field
DocType
artificial neural networks,smartphone apps,privacy,security,sentiment
Ranking,ALARM,Computer security,Computer science,Installation,Exploit,Security analysis,Artificial neural network
Conference
ISSN
ISBN
Citations 
1071-6572
978-1-5386-1586-7
0
PageRank 
References 
Authors
0.34
9
2
Name
Order
Citations
PageRank
majid hatamian1213.86
Jetzabel Serna2306.39