Carla E. Brodley,
School of Electrical and Computer Engineering,
West Lafayette, IN 47906
Department of Computer Sciences
Florida Institute of Technology
Melbourne, FL 32901
In the past few years there has been a monumental surge of interest in computer security in the private, public, university and government sectors. This tutorial consists of an introduction to computer security as well as an overview of existing research on applications of KDD to computer security. For KDD researchers and practitioners, the tutorial will provide background knowledge and opportunities for applying KDD to computer security. For computer security researchers and practitioners, it provides knowledge on how KDD can benefit and enhance computer security.
The expected audience for this tutorial is KDD practitioners interested in applying KDD to a new application domain, KDD researchers interested in KDD issues related to computer security, and computer security professional/researchers/government employees interested in the state of the art in applications of KDD to security. The audience is not expected to be familiar with computer security. However, the audience is expected to have basic knowledge in computer science and KDD.
Carla E. Brodley is an associate professor in the School of Electrical and Computer Engineering at Purdue University. She received her bachelors degree from McGill University in 1985 and her PhD in computer science from the University of Massachusetts in 1994. In 2001 she served as program co-chair for the International Conference on Machine Learning. Currently she is an associate editor of the Journal of Artificial Intelligence Research and serves on the editorial board of the Journal of Machine Learning Research. She has worked in the areas of intrusion detection, hardware support for security, anomaly detection in networks, classifier formation, and feature selection for unsupervised learning. Prof Brodley has taught undergraduate computer security classes at Purdue and graduate classes on data mining and machine learning.
Philip Chan is an associate professor of computer science at Florida Institute of Technology. He is currently on sabbatical leave at Laboratory of Computer Science, Massachusetts Institute of Technology. He received his PhD, MS, and BS in computer science from Columbia University, Vanderbilt University, and Southwest Texas State University respectively. His main research interests include scalable adaptive methods, machine learning, data mining, distributed and parallel computing, and intelligent systems. His recent research focuses on machine learning techniques for anomaly detection. He has published papers and received support from DARPA in the area of machine learning and intrusion detection. Prof. Chan has served as program committee members for the major data mining conferences: KDD, ICDM, and SDM, and is on the editorial board of Journal of Database Management. With Prof. Chan's efforts, ICDM 2003 will be held in Melbourne, Florida. He co-edited the book "Advances in Parallel and Distributed Knowledge Discovery," AAAI/MIT Press, 2000.