Publications

DBLP Bibliography: Philip K. Chan, Philip Chan

Advances in Distributed and Parallel Knowledge Discovery
Hillol Kargupta and Philip Chan (editors)
AAAI/MIT Press, 2000
ISBN: 0-262-61155-4

Available at:
AAAI/MIT Press,
amazon.com,
barnesandnoble.com

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Learning Implicit User Interest Hierarchy for Context in Personalization. H. Kim and P. Chan. Applied Intelligence, 28(2):153-166, 2008.
Intelligent Systems at Florida Tech. P. Chan, R. Menezes, D. Mitra, E. Ribeiro, and M. Silaghi. IEEE Intelligent Informatics Bulletin, 8(1):5-6, 2007.
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.
Toward accurate dynamic time wrapping in linear time and space.. S. Salvador and P. Chan. Intelligent Data Analysis, 11(5):561-580, 2007.
Machine Learning for Computer Security. P. Chan & R. Lippmann. Journal of Machine Learning Research, 7:2669-2672, 2006.
Personalized Ranking of Search Results with Learned User Interest Hierarchies from Bookmarks. H. Kim & P. Chan. In Advances in Web Mining and Web Usage Analysis (LNCS 4198), O. Nasraoui, O. Zaine, M. Spiliopolou, B. Mobasher, B. Masand & P. Yu (editors) pp 158-176, Springer, 2006.
On the Learning of System Call Attributes for Host-based Anomaly Detection. G. Tandon and P. Chan. International Journal on Artificial Intelligence Tools, 15(6):875-892, 2006.
Data cleaning and enriched representations for anomaly detection in system calls. G. Tandon, P. Chan, and D. Mitra. In Machine Learning and Data Mining for Computer Security: Methods and Applicatioins, M. Maloof (editor), Springer, pp. 137-156, 2006.
Modeling Multiple Time Series for Anomaly Detection. P. Chan & M. Mahoney. Proc. IEEE Intl. Conf. on Data Mining, pp. 90-97, 2005.
Trajectory Boundary Modeling of Time Series for Anomaly Detection. M. Mahoney and P. Chan. Workshop on Data Mining Methods for Anomaly Detection, KDD Conf., 2005.
Personalized Ranking of Search Results with Learned User Interest Hierarchies from Bookmarks. H. Kim and P. Chan. WEBKDD Workshop, KDD Conf., 2005.
Implicit Indicators for Interesting Web Pages. H. Kim and P. Chan. Proc. Intl. Conf. on Web Information Systems and Technologies, pp. 270-277, 2005.
Project EMD-MLR: Educational Material Development and Research in Machine Learning for Undergraduate Students. G. Anagnostopoulos, M. Geogiopoulos, K. Ports, S. Richie, N. Cardinale, M. White, V. Kepuska, P. Chan, A. Wu & M. Kysilka. Proc. 2005 ASEE Annual Conf. and Exposition, 2005.
Learning Useful System Call Attributes for Anomaly Detection. G. Tandon & P. Chan. Proc. 18th Intl. FLAIRS Conf., pp. 405-410, 2005.
Learning States and Rules for Detecting Anomalies in Time Series. S. Salvador & P. Chan. Applied Intelligence, 23(3):241-255, 2005.
Identifying Variable-Length Meaningful Phrases with Correlation Functions. H. Kim & P. Chan. Proc. 16th IEEE Intl. Conf. on Tools with AI, pp. 30-38, 2004.
Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms. S. Salvador & P. Chan. Proc. 16th IEEE Intl. Conf. on Tools with AI, pp. 576-584, 2004.
MORPHEUS: Motif Oriented Representations to Purge Hostile Events from Unlabeled Sequences. G. Tandon, P. Chan, and D. Mitra. In Workshop on Visualization and Data Mining for Computer Security (Viz/DMSEC), 11th ACM Conf. on Computer and Communications Security (CCS), pp. 16-25, 2004.
FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space. S. Salvador & P. Chan. KDD Workshop on Mining Temporal and Sequential Data, pp. 70-80, 2004.
Learning States and Rules for Time Series Anomaly Detection. S. Salvador, P. Chan & J. Brodie. Proc. 17th Intl. FLAIRS Conf., pp. 300-305, 2004.
Motif-oriented Representation of Sequences for a Host-based Intrusion Detection System. G. Tandon, D. Mitra & P. Chan. 17th Intl. Conf. on Industrial & Engineering Applications of AI & Expert Systems, pp. 605-615, 2004.
Learning Rules for Anomaly Detection of Hostile Network Traffic. M. Mahoney & P. Chan. Proc. Third IEEE Intl. Conf. on Data Mining (ICDM), pp. 601-4, 2003.
Learning Rules from System Call Arguments and Sequences for Anomaly Detection. G. Tandon & P. Chan. ICDM Workshop on Data Mining for Computer Security (DMSEC), pp. 20-29, 2003.
Boundary Detection in Tokenizing Network Application Payload for Anomaly Detection. R. Vargiya & P. Chan. ICDM Workshop on Data Mining for Computer Security (DMSEC), pp. 50-59, 2003.
Learning Rules and Clusters for Anomaly Detection in Network Traffic. P. Chan, M. Mahoney & M. Arshad. In Managing Cyber Threats: Issues, Approaches and Challenges, V. Kumar, J. Srivastava & A. Lazarevic (editors), Springer, pp. 81-99, 2005.
An Analysis of the 1999 DARPA/Lincoln Laboratory Evaluation Data for Network Anomaly Detection. M. Mahoney and P. Chan. Proc. 6th Intl. Symp. Recent Advances in 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, p. 101-108, 2003.
Learning Nonstationary Models of Normal Network Traffic for Detecting Novel Attacks. M. Mahoney and P. Chan. Proc. Eighth Intl. Conf. Knowledge Discovery and Data Mining, p376-385, 2002.
Using Artificial Anomalies to Detect Unknown and Known Network Intrusions. W. Fan, M. Miller, S. Stolfo, W. Lee, P. Chan. IEEE Intl. Conf. Data Mining, pp. 123-130, 2001.
A Protocol Language Approach to Generating Client-Server Software. M. Douglas and P. Chan. Proc. of Thirteenth Intl. Conf. Parallel and Distributed Computing and Systems, 2001, pp 649-654.
Distributed Communication for Highly Mobile Agents. M. Samarah and P. Chan. Fourth Pacific Rim Intl. Workshop on Multi-agents, 2001.
Real Time Data Mining-based Intrusion Detection. W. Lee, S. Stolfo, P. Chan, E. Eskin, W. Fan, M. Miller, S. Hershkop, and J. Zhang. Proc. Second DARPA Information Survivability Conference and Exposition, pp. I85-100, 2001.
Constructing web user profiles: A non-invasive learning approach. P. Chan, in Web Usage Analysis and User Profiling. LNAI 1836, Springer-Verlag, p39-55, 2000.
Advances in Distributed and Parallel Knowledge Discovery. H. Kargupta and P. Chan (editors), AAAI/MIT Press, 2000.
Cost-based Modeling for Fraud and Instrusion Detection: Results from the JAM Project. S. Stolfo, W. Fan, W. Lee, A. Prodromidis, and P. Chan, Proc. DARPA Information Survivability Conference and Exposition. IEEE Computer Press, p. II 130-144, 2000.
Meta-learning in distributed data mining systems: Issues and approaches. A. Prodromidis, P. Chan, and S. Stolfo. In Advances in Distributed and Parallel Knowledge Discovery, H. Kargupta and P. Chan (editors), Chapter 3, AAAI/MIT Press, 2000.
Distributed data mining in credit card fraud detection. P. Chan, W. Fan, A. Prodromidis, and S. Stolfo. IEEE Intelligent Systems, 14(6):67-74, 1999.
A non-invasive learning approach to building web user profiles. P. Chan. KDD-99 Workshop on Web Usage Analysis and User Profiling, pp. 7-12, 1999. [data used in the study]
AdaCost: Misclassification Cost-sensitive Boosting. W. Fan, S. Stolfo, J. Zhang, and P. Chan. Proc. Sixteenth Intl. Conf. Machine Learning, pp. 99-105, 1999.
Using Conflicts among Base Classifiers to Measure the Performance of Stacking. W. Fan, S. Stolfo, and P. Chan. ICML-99 Workshop on Recent Advances in Meta-learning and Future Work, pp.10-17, 1999.
Toward Scalable Learning with Non-uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection. P. Chan and S. Stolfo. Proc. Fourth Intl. Conf. Knowledge Discovery and Data Mining, p164-168, 1998. [PDF]
Learning with Non-uniform Class and Cost Distributions: Effects and a Distributed Multi-classifier Approach. P. Chan and S. Stolfo. Work. Notes KDD-98 Workshop on Distributed Data Mining, p1-9, 1998.
JAM: Java Agents for Meta-Learning over Distributed Databases. S. Stolfo, A. Prodromidis, S. Tselepis, W. Lee, W. Fan, and P. Chan. Proc. Third Intl. Conf. Knowledge Discovery and Data Mining, p74-81, 1997.
Credit Card Fraud Detection Using Meta-Learning: Issues and Initial Results. S. Stolfo, W. Fan, W. Lee, A. Prodromidis, and P. Chan. Work. Notes AAAI-97 Workshop on AI Approaches to Fraud Detection and Risk Management, 1997.
Learning Patterns from Unix Process Execution Traces for Intrusion Detection. W. Lee, S. Stolfo, and P. Chan. Work. Notes AAAI-97 Workshop on AI Approaches to Fraud Detection and Risk Management, 1997.
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