NDLI: Casting out Demons: Sanitizing Training Data for Anomaly Sensors

Content Provider	IEEE Xplore Digital Library
Author	Cretu, G.F. Stavrou, A. Locasto, M.E. Stolfo, S.J. Keromytis, A.D.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci., Columbia Univ., Columbia, NY (Cretu, G.F.)
Abstract	The efficacy of anomaly detection (AD) sensors depends heavily on the quality of the data used to train them. Artificial or contrived training data may not provide a realistic view of the deployment environment. Most realistic data sets are dirty; that is, they contain a number of attacks or anomalous events. The size of these high-quality training data sets makes manual removal or labeling of attack data infeasible. As a result, sensors trained on this data can miss attacks and their variations. We propose extending the training phase of AD sensors (in a manner agnostic to the underlying AD algorithm) to include a sanitization phase. This phase generates multiple models conditioned on small slices of the training data. We use these "micro- models" to produce provisional labels for each training input, and we combine the micro-models in a voting scheme to determine which parts of the training data may represent attacks. Our results suggest that this phase automatically and significantly improves the quality of unlabeled training data by making it as "attack-free" and "regular" as possible in the absence of absolute ground truth. We also show how a collaborative approach that combines models from different networks or domains can further refine the sanitization process to thwart targeted training or mimicry attacks against a single site.
Starting Page	81
Ending Page	95
File Size	381986
Page Count	15
File Format	PDF
ISBN	9780769531687
ISSN	10816011
DOI	10.1109/SP.2008.11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Casting Training data Traffic control Intrusion detection Computer security Data security Data privacy Computer science Telecommunication traffic Application software
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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