Chan, Philip

Associate Professor
Computer Sciences and Cybersecurity

Educational Background

B.S., Computer Science Southwest Texas State University 1986
M.S., Computer Science Vanderbilt University 1988
Ph.D., Computer Science Columbia University 1996

Professional Experience

Associate Professor, Computer Science, Florida Institute of Technology (2002-present); Assistant Professor (1997-2002); Visiting Assistant Professor (1995-97).

Visiting Scientist/Faculty (sabbatical), Laboratory of Computer Science, Massachusetts Institute of Technology (2002-3).

Research Assistant, Department of Computer Science, Columbia University (1988-95). Ported an OPS5-to-C compiler to the DADO parallel machines, built a financial expert system, and working on scalable machine learning (Prof. Stolfo).

Research Intern, Learning Systems Department, Siemens Corporate Research (Summer, 1993); Designed and implemented the automated revision component for a CAT scanner diagnostic knowledge base (Drs. Drastal & Langley).

Research Intern, Knowledge-Based Systems Department, GTE Labs. (Summer, 1991 & 1992); Designed and built a distributed multi-agent telecommunication network problem-solving system and tools for an integrated knowledge discovery system (Drs. Weihmayer, Piatetsky-Shapiro & Matheus).

Research Intern, Citibank (Summer, 1990)
Deployed a mortgage pool allocation expert system and linked it to the CTS database (Dr. Schutzer).

Research Programmer, Department of Chemistry, Vanderbilt University (Summer, 1988); Ported theoretical chemistry programs to the SCS supercomputer.

Current Research

• Adaptive web personalization
• Anomaly detection for computer security
• Anomaly detection for device monitoring

Selected Publications

``Increasing coverage to improve detection of network and host anomalies.'' G. Tandon and P. Chan. Machine Learning, 79(3):307-334, 2010.

``Learning Implicit User Interest Hierarchy for Context in Personalization.'' H. Kim and P. Chan. Applied Intelligence, 28(2):153-166, 2008.

``Weighting versus Pruning in Rule Validation for Detecting Network and Host Anomalies.'' G. Tandon & P. Chan. Proc. ACM Intl. Conf. on Knowledge Discovery and Data Mining (KDD). pp. 697-706, 2007.

``Modeling Multiple Time Series for Anomaly Detection.'' P. Chan & M. Mahoney. Proc. IEEE Intl. Conf. on Data Mining, pp. 90-97, 2005.

``Learning States and Rules for Detecting Anomalies in Time Series.''
S. Salvador and P. Chan, Applied Intelligence, 23(3):241-255, 2005.

``Learning Rules for Anomaly Detection of Hostile Network Traffic,'' M. Mahoney and P. Chan,
Proc. Third IEEE International Conference on Data Mining, pp. 601-4, 2003.

``An Analysis of the 1999 DARPA/Lincoln Laboratory Evaluation Data for Network Anomaly,'' M. Mahoney and P. Chan, Proc. 6th Intl. Symp. Recent Advances on Intrusion Detection, pp. 220-237, 2003.

``Learning Implicit User Interest Hierarchy for Context in Personalization,'' H. Kim and P. Chan, Proc. Intl. Conf. on Intelligent User Interfaces, pp. 101-108, 2003.

``Learning Nonstationary Models of Normal Network Traffic for Detecting Novel Attacks,'' M. Mahoney and P. Chan, Proc. Eighth Intl. Conf. on Knowledge Discovery and Data Mining, pp. 376-385, 2002.

Advances in Distributed and Parallel Knowledge Discovery, H. Kargupta and P. Chan (editors),
AAAI/MIT Press, 2000.