Displaying publications 1 to 6 of 6 publications associated with the Machine Learning Group in 2005:
Authors: Early, J. P. and Brodley, C. E.
Machine Learning and Data Mining for Computer Security: Methods and Applications
pp. 107-124, Springer
Authors: Fern, X. Z., Brodley, C. E., Friedl, M. A.
SIAM International Conference on Data Mining
Abstract: This paper addresses the task of analyzing the correlation between two related domains X and Y. Our research is motivated by an Earth Science task that studies the relationship between vegetation and precipitation. A standard statistical technique for such problems is Canonical Correlation Analysis (CCA). A critical limitation of CCA is that it can only detect linear correlation between the two domains that is globally valid throughout both data sets. Our approach addresses this limitation by constructing a mixture of local linear CCA models through a process we name correlation clustering. In correlation clustering, both data sets are clustered simultaneously according to the data's correlation structure such that, within a cluster, domain X and domain Y are linearly correlated in the same way. Each cluster is then analyzed using the traditional CCA to construct local linear correlation models. We present results on both artificial data sets and Earth Science data sets to demonstrate that the proposed approach can detect useful correlation patterns, which traditional CCA fails to discover.
Authors: Kuperman, B., Brodley, C., Ozdoganoglu, H., Vijaykumar, T.N. and Jalote, A.
Communications of the ACM
48 (11), pp. 51-56, November
Abstract: How to mitigate remote attacks that exploit buffer overflow vulnerabilities on the stack and enable attackers to take control of the program.
Authors: Khardon, R., D. Roth, R. A. Servedio
Journal of Artificial Intelligence Research
vol. 24 , pp. 341-356
Authors: Hasan, J., Jalote, A., Vijaykumar, T. N., and Brodley, C. E.
Proceedings of the 11th International Symposium on High-Performance Computer Architecture
Abstract: In the past, there have been several denial-of-service (DOS) attacks which exhaust some shared resource (e.g., physical memory, process table, file descriptors, TCP connections) of the targeted machine. Though these attacks have been addressed, it is important to continue to identify and address new attacks because DOS is one of most prominent methods used to cause significant financial loss. A recent paper shows how to prevent attacks that exploit the sharing of pipeline resources (e.g., shared trace cache) in SMT to degrade the performance of normal threads. In this paper, we show that power density can be exploited in SMT to launch a novel DOS attack, called heat stroke. Heat stroke repeatedly accesses a shared resource to create a hot spot at the resource. Current solutions to hot spots inevitably involve slowing down the pipeline to let the hot spot cool down. Consequently, heat stroke slows down the entire SMT pipeline and severely degrades normal threads. We present a solution to heat stroke by identifying the thread that causes the hot spot and selectively slowing down the malicious thread while minimally affecting normal threads.
Authors: Khardon, R., Servedio, R.
Journal of Machine Learning Research
vol. 6 pp. 1405-1429