Philip K. Chan spends much of his time researching Intrusion detection system, Data mining, Artificial intelligence, Machine learning and Scalability. His Intrusion detection system study incorporates themes from Anomaly detection, Set, Distributed database and Protocol. His Anomaly detection research is multidisciplinary, relying on both False alarm and Source address.
His Data mining study incorporates themes from Exploit, Event, Hierarchical clustering of networks and Unsupervised learning. His research integrates issues of Database and Pattern recognition in his study of Artificial intelligence. His studies in Machine learning integrate themes in fields like Knowledge acquisition, Knowledge extraction, Data science and Knowledge-based systems.
Philip K. Chan spends much of his time researching Artificial intelligence, Data mining, Anomaly detection, Machine learning and Intrusion detection system. His work focuses on many connections between Artificial intelligence and other disciplines, such as Pattern recognition, that overlap with his field of interest in Outlier. The various areas that Philip K. Chan examines in his Data mining study include Scalability, Training set, Data science and Pruning.
He interconnects Real-time computing, System call, Host and Cluster analysis in the investigation of issues within Anomaly detection. His work on Instance-based learning, Semi-supervised learning and Active learning as part of his general Machine learning study is frequently connected to Multi-task learning and Meta learning, thereby bridging the divide between different branches of science. His Intrusion detection system research includes elements of False alarm and Set.
Philip K. Chan mainly investigates Malware, Artificial intelligence, Data mining, Open set and Artificial neural network. His Malware study integrates concerns from other disciplines, such as Sample, World Wide Web, Cluster analysis and Subroutine. The study incorporates disciplines such as Time complexity, Scalability and Feature extraction in addition to Cluster analysis.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning, Wearable computer and Computer vision. His work carried out in the field of Machine learning brings together such families of science as Domain, Representation, Training set and Image. Philip K. Chan is interested in Measure, which is a field of Data mining.
His scientific interests lie mostly in Malware, Theoretical computer science, Artificial neural network, Representation and Open set. The concepts of his Malware study are interwoven with issues in Scalability, Data mining and Static analysis. Philip K. Chan has included themes like Feature extraction, Cluster analysis and Subroutine in his Scalability study.
His Subroutine study frequently draws connections between adjacent fields such as Time complexity. His research in Static analysis intersects with topics in Statement, Training time and Opcode. His research on Theoretical computer science often connects related areas such as Class.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Toward accurate dynamic time warping in linear time and space
Stan Salvador;Philip Chan.
intelligent data analysis (2007)
Distributed data mining in credit card fraud detection
P.K. Chan;W. Fan;A.L. Prodromidis;S.J. Stolfo.
IEEE Intelligent Systems & Their Applications (1999)
AdaCost: Misclassification Cost-Sensitive Boosting
Wei Fan;Salvatore J. Stolfo;Junxin Zhang;Philip K. Chan.
international conference on machine learning (1999)
Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms
S. Salvador;P. Chan.
international conference on tools with artificial intelligence (2004)
An analysis of the 1999 DARPA/lincoln Laboratory evaluation data for network anomaly detection
Matthew V. Mahoney;Philip K. Chan.
Lecture Notes in Computer Science (2003)
Cost-based modeling for fraud and intrusion detection: results from the JAM project
S.J. Stolfo;Wei Fan;Wenke Lee;A. Prodromidis.
darpa information survivability conference and exposition (2000)
Learning nonstationary models of normal network traffic for detecting novel attacks
Matthew V. Mahoney;Philip K. Chan.
knowledge discovery and data mining (2002)
Learning Patterns from Unix Process Execution Traces for Intrusion Detection
Wenke Lee;Saivatore J. Stolfo;Philip K. Chan.
Toward scalable learning with non-uniform class and cost distributions: a case study in credit card fraud detection
Philip K. Chan;Salvatore J. Stolfo.
knowledge discovery and data mining (1998)
JAM: java agents for meta-learning over distributed databases
Salvatore Stolfo;Andreas L. Prodromidis;Shelley Tselepis;Wenke Lee.
knowledge discovery and data mining (1997)
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