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Author Rndic, N. ♦ Laskov, P.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2014
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Portable document format ♦ Training ♦ Learning systems ♦ Taxonomy ♦ Malware ♦ Feature extraction
Abstract Learning-based classifiers are increasingly used for detection of various forms of malicious data. However, if they are deployed online, an attacker may attempt to evade them by manipulating the data. Examples of such attacks have been previously studied under the assumption that an attacker has full knowledge about the deployed classifier. In practice, such assumptions rarely hold, especially for systems deployed online. A significant amount of information about a deployed classifier system can be obtained from various sources. In this paper, we experimentally investigate the effectiveness of classifier evasion using a real, deployed system, PDFrate, as a test case. We develop a taxonomy for practical evasion strategies and adapt known evasion algorithms to implement specific scenarios in our taxonomy. Our experimental results reveal a substantial drop of PDFrate's classification scores and detection accuracy after it is exposed even to simple attacks. We further study potential defense mechanisms against classifier evasion. Our experiments reveal that the original technique proposed for PDFrate is only effective if the executed attack exactly matches the anticipated one. In the discussion of the findings of our study, we analyze some potential techniques for increasing robustness of learning-based systems against adversarial manipulation of data.
Description Author affiliation: Dept. of Cognitive Syst., Univ. of Tubingen, Tubingen, Germany (Rndic, N.; Laskov, P.)
ISBN 9781479946860
ISSN 10816011
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-05-18
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 331.63 kB
Page Count 15
Starting Page 197
Ending Page 211


Source: IEEE Xplore Digital Library