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2012 IEEE 12th International Conference on Data Mining (2012)
Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 13, 2012
ISSN: 1550-4786
ISBN: 978-1-4673-4649-8
pp: 1206-1211
Data mining tasks are made more complicated when adversaries attack by modifying malicious data to evade detection. The main challenge lies in finding a robust learning model that is insensitive to unpredictable malicious data distribution. In this paper, we present a sparse relevance vector machine ensemble for adversarial learning. The novelty of our work is the use of individualized kernel parameters to model potential adversarial attacks during model training. We allow the kernel parameters to drift in the direction that minimizes the likelihood of the positive data. This step is interleaved with learning the weights and the weight priors of a relevance vector machine. Our empirical results demonstrate that an ensemble of such relevance vector machine models is more robust to adversarial attacks.
kernel parameter learning, adversarial learning, spare Bayesian learning, relevance vector machine

Y. Zhou, M. Kantarcioglu and B. Thuraisingham, "Sparse Bayesian Adversarial Learning Using Relevance Vector Machine Ensembles," 2012 IEEE 12th International Conference on Data Mining(ICDM), Brussels, Belgium Belgium, 2012, pp. 1206-1211.
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