Title
Quantification of side-channel information leaks based on data complexity measures for web browsing.
Abstract
Website fingerprinting attack can identify the visited websites by analyzing the side-channel information of the network traffic even though it is transferred through an encrypted tunnel. The security of web browsing can be evaluated by quantifying the side-channel information leaks. However, most of the current leak quantification measures focus on web applications and may be impractical in web browsing due to their time complexity. Although the revised models were proposed to simplify computations, their assumptions may not be suitable for web browsing. In this paper, the problem of website fingerprinting is analyzed from the viewpoint of pattern classification. The data complexity measures, which quantify the difficulty of separating classes in a classification problem, are applied to describe the leak quantification. The performance of these data complexity measures in representing information leaks is discussed and compared with the existing approaches. This comparative analysis is realized conceptually and through experiments by using two website fingerprinting countermeasures: traffic morphing and BuFLO. Moreover, the parameter selection model based on the leak quantification is proposed to estimate suitable parameters for the website fingerprinting countermeasure. The experimental results confirm that the countermeasures with parameters selected according to the data complexity measures are more secure than other leak quantification measures.
Year
DOI
Venue
2015
10.1007/s13042-015-0348-3
Int. J. Machine Learning & Cybernetics
Keywords
Field
DocType
Data complexity measures, Side-channel leak quantification, Web browsing, Traffic analysis countermeasure
Morphing,Data mining,Computer science,Encryption,Side channel attack,Web navigation,Web application,Time complexity,Data complexity,Computation
Journal
Volume
Issue
ISSN
6
4
1868-808X
Citations 
PageRank 
References 
3
0.45
33
Authors
5
Name
Order
Citations
PageRank
Zhimin He153635.90
Patrick P. K. Chan227133.82
Daniel S. Yeung3112692.97
W. Pedrycz4139661005.85
Wing W. Y. Ng552856.12