NDLI: Adversarial classification

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Adversarial classification

Content Provider	ACM Digital Library
Author	Verma, Deepak Sanghai, Sumit Domingos, Pedro Dalvi, Nilesh
Abstract	Essentially all data mining algorithms assume that the data-generating process is independent of the data miner's activities. However, in many domains, including spam detection, intrusion detection, fraud detection, surveillance and counter-terrorism, this is far from the case: the data is actively manipulated by an adversary seeking to make the classifier produce false negatives. In these domains, the performance of a classifier can degrade rapidly after it is deployed, as the adversary learns to defeat it. Currently the only solution to this is repeated, manual, ad hoc reconstruction of the classifier. In this paper we develop a formal framework and algorithms for this problem. We view classification as a game between the classifier and the adversary, and produce a classifier that is optimal given the adversary's optimal strategy. Experiments in a spam detection domain show that this approach can greatly outperform a classifier learned in the standard way, and (within the parameters of the problem) automatically adapt the classifier to the adversary's evolving manipulations.
Starting Page	99
Ending Page	108
Page Count	10
File Format	PDF
ISBN	1581138881
DOI	10.1145/1014052.1014066
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-08-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Cost-sensitive learning Integer linear programming Naive bayes Game theory Spam detection
Content Type	Text
Resource Type	Article

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