Displaying publications 1 to 7 of 7 publications associated with the Machine Learning Group in 2004:
Authors: M. Arias and R. Khardon
In The Proceedings of the International Conference on Inductive Logic Programming
Authors: Bajcsy, et al
Communications of the ACM
vol 47, issue 3, pp. 58-61, March
Abstract: Creating an experimental infrastructure for developing next-generation information security technologies.
Authors: Dy, J. and Brodley, C. E.
Journal of Machine Learning Research
5, pp. 845-889, August
Abstract: In this paper, we identify two issues involved in developing an automated feature subset selection algorithm for unlabeled data: the need for finding the number of clusters in conjunction with feature selection, and the need for normalizing the bias of fe ature selection criteria with respect to dimension. We explore the feature selection problem and these issues through FSSEM (Feature Subset Selection using Expectation-Maximization (EM) clustering) and through two different performance criteria for evaluating candidate feature subsets: scatter separability and maximum likelihood. We present proofs on the dimensionality biases of these feature criteria, and present a cross-projection normalization scheme that can be applied to any criterion to ameliorate these biases. Our experiments show the need for feature selection, the need for addressing these two issues, and the effectiveness of our proposed solutions.
Authors: Scabuk, C., Brodley, C. E., and Shields, T. C.
ACM Conference on Computer and Communications Security
Abstract: A network covert channel is a mechanism that can be used to leak information across a network in violation of a security policy and in a manner that can be difficult to detect. In this paper, we describe our implementation of a covert network timing channel, discuss the subtle issues that arose in its design, and present performance data for the channel. We then use our implementation as the basis for our experiments in its detection. We show that the regularity of a timing channel can be used to differentiate it from other traffic and present two methods of doing so and measures of their efficiency. We also
investigate mechanisms that attackers might use to disrupt the regularity of the timing channel, and demonstrate methods of detection that are effective against them.
Authors: N. Abe, R. Khardon and T. Zeugmann (Editors)
Theoretical Computer Science
Volume 313, Issue 2, Pages 173-312