Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Combining Multiple Weak Clusterings
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Do security toolbars actually prevent phishing attacks?
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Cache Cookies for Browser Authentication (Extended Abstract)
SP '06 Proceedings of the 2006 IEEE Symposium on Security and Privacy
Learning to detect phishing emails
Proceedings of the 16th international conference on World Wide Web
Modified global k-means algorithm for clustering in gene expression data sets
WISB '06 Proceedings of the 2006 workshop on Intelligent systems for bioinformatics - Volume 73
Modified global k-means algorithm for minimum sum-of-squares clustering problems
Pattern Recognition
Detection of CAN by ensemble classifiers based on ripple down rules
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
A multi-tier ensemble construction of classifiers for phishing email detection and filtering
CSS'12 Proceedings of the 4th international conference on Cyberspace Safety and Security
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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This paper describes a novel approach to profiling phishing emails based on the combination of multiple independent clusterings of the email documents. Each clustering is motivated by a natural representation of the emails. A data set of 2048 phishing emails provided by a major Australian financial institution was pre-processed to extract features describing the textual content, hyperlinks and orthographic structure of the emails. Independent clusterings using different techniques were performed on each representation, and these clusterings were then ensembled using a variety of consensus functions. This paper concentrates on using several clustering approaches to determine the most likely number of phishing groups and explores ways in which individual and combined results relate. The approach suggests a number of phishing groups and the structure of the approach can aid the development of profiles based on the individual clusters. The actual profiling is not carried out in this paper.