NDLI: Practical Evasion of a Learning-Based Classifier: A Case Study

Content Provider	IEEE Xplore Digital Library
Author	Rndic, N. Laskov, P.
Copyright Year	2014
Description	Author affiliation: Dept. of Cognitive Syst., Univ. of Tubingen, Tubingen, Germany (Rndic, N.; Laskov, P.)
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.
Starting Page	197
Ending Page	211
File Size	339594
Page Count	15
File Format	PDF
ISBN	9781479946860
ISSN	10816011
DOI	10.1109/SP.2014.20
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Portable document format Training Learning systems Taxonomy Malware Feature extraction
Content Type	Text
Resource Type	Article

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