Short Course: Mining Data Streams for Intrusion Detection and Security Protection
COURSE DESCRIPTION:
This tutorial is designed to provide an overview of issues related to mining data streams for security protection. The tutorial should benefit researchers as well as practitioners from industry and academia who are interested in areas related to data mining, intrusion detection, and information security. Copies of slides prepared using PowerPoint will be distributed to the attendees. Duration is 1 day (or 2 half-days).
PREREQUISITE: Basic knowledge of data structures, algorithms, statistics, database systems and artificial intelligence is preferred.
HIGH-LEVEL COURSE OUTLINE:
- Introduction
- Introduction to data mining
- Data mining and security protection
- Data mining: Concepts and methods
- A short introduction to data mining
- Major data mining methods: Data generalization, characterization, association, classification, clustering, and outlier analysis techniques
- Principles of stream data mining
- Stream data processing and stream data mining: The major technical challenges
- Stream OLAP and stream data cubes
- Stream frequent pattern analysis
- Stream classification
- Clustering dynamic and evolving data streams
- Intrusion detection
- Major methods of intrusion detection
- Data mining for intrusion detection
- Anomaly and intrusion detection by mining data streams
- Protection of information security in data mining
- Data mining and security protection
- Privacy-preserving data mining
- Protection of information security in data mining
- Security protection in a dynamic and evolving streaming environment
- Research problems in data mining for security protection: An open forum discussion
- Conclusions
Instructor Biography
Jiawei Han received the Ph.D. from the University of Wisconsin at Madison. He is currently a Professor of Computer Science at the University of Illinois at Urbana-Champaign. Previously, he was an Endowed University Professor at Simon Fraser University, Canada. He has been working on research into data mining, data warehousing, stream data mining, spatial and multimedia data mining, and bio-medical data mining, with over 300 conference and journal publications. He has chaired or served in over 100 program committees of international conferences and workshops, including ACM SIGKDD Conferences (2001 best paper award chair, 2002 student award chair, 1996 PC co-chair), SIAM-Data Mining Conferences (2001 and 2002 PC co-chair), ACM SIGMOD Conferences (2000 exhibit program chair), International Conferences on Data Engineering (2004 and 2002 PC vice-chair), International Conferences on Data Mining (2005 PC co-chair) and International Conference on Very Large Data Bases (2006 VLDB Americas Chair). He also served or is serving on the editorial boards for Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, the Journal of Intelligent Information Systems, and the Journal of Computer Science and Technology. He is currently serving on the Board of Directors for the Executive Committee of the ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD). Jiawei has received three IBM Faculty Awards (2002-2006), the Outstanding Contribution Award at the 2002 International Conference on Data Mining, the ACM Service Award (1999), and the ACM SIGKDD Innovations Award (2004). He is an ACM Fellow (since 2003). He is the first author of the textbook Data Mining: Concepts and Techniques (Morgan Kaufmann, 2001).