DocumentCode
2084837
Title
On countermeasures to traffic analysis attacks
Author
Fu, Xinwen ; Graham, Bryan ; Bettati, Riccardo ; Zhao, Wei
Author_Institution
Dept. of Comput. Sci., Texas A&M Univ., College Station, TX, USA
fYear
2003
fDate
18-20 June 2003
Firstpage
188
Lastpage
195
Abstract
We make three contributions. First, we propose Shannon´s perfect secrecy theory as a foundation for developing countermeasures to traffic analysis attacks on information security systems. A system violating the perfect secrecy conditions can leak mission critical information. Second, we suggest statistical pattern recognition as a fundamental technology to test an information system´s security. This technology can cover a large category of testing approaches because of statistical pattern recognition´s maturity and abundant techniques. Third, researchers have proposed traffic padding as countermeasures to traffic analysis attacks. By applying the proposed information assurance testing framework, we find that constant rate traffic padding does not satisfy Shannon´s perfect secrecy conditions because of its implementation mechanism. We design a variant rate traffic padding strategy as an alternative, which is validated by both theoretical analysis and empirical results.
Keywords
computer crime; data privacy; information theory; internetworking; statistical analysis; telecommunication security; telecommunication traffic; Shannon perfect secrecy theory; constant rate traffic padding; information assurance testing framework; information security systems; mission critical information; statistical pattern recognition; traffic analysis attacks; variant rate traffic padding; Computer science; Contracts; Cryptography; Information analysis; Information security; Mission critical systems; Pattern recognition; Payloads; Protection; System testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Assurance Workshop, 2003. IEEE Systems, Man and Cybernetics Society
Print_ISBN
0-7803-7808-3
Type
conf
DOI
10.1109/SMCSIA.2003.1232420
Filename
1232420
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