Autonomic Computing studies techniques for making computing systems
self-configuring, self-tuning, self-diagnosing, self-repairing, and
self-protecting. Our current work in the area focuses on using machine learning
techniques to allow computing systems to configure and tune themselves to adapt
to changing workloads and to automatically acquire patterns for detecting and
blocking Internet worms.
Publications
- Fast and Effective Worm Fingerprinting via Machine Learning [Abstract] [PDF]
Stewart Yang, Jianping Song, Harish Rajamani, Taewon Cho, Yin Zhang and Raymond Mooney
Technical Report AI-06-335, Artificial Intelligence Lab, The University of Texas at Austin, August 2006.
This is a longer version of our ICAC-2006 paper.
- Fast and Effective Worm Fingerprinting via Machine Learning [Abstract] [PDF]
Stewart Yang, Jianping Song, Harish Rajamani, Taewon Cho, Yin Zhang and Raymond Mooney
In Proceedings of the 3rd IEEE International Conference on Autonomic Computing (ICAC-2006), Poster Session, Dublin, Ireland, June 2006.
- Towards Self-Configuring Hardware for Distributed Computer Systems [Abstract] [PDF]
Wildstrom, J., Stone, P., Witchel, E., Mooney, R., and Dahlin, M.
In Proceedings of the Second IEEE International Conference on Autonomic Computing (ICAC-2005), Seattle, WA, pp. 241-249, June 2005.
mooney@cs.utexas.edu