C4.5: programs for machine learning
C4.5: programs for machine learning
Protecting Users Against Phishing Attacks with AntiPhish
COMPSAC '05 Proceedings of the 29th Annual International Computer Software and Applications Conference - Volume 01
Do security toolbars actually prevent phishing attacks?
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An Antiphishing Strategy Based on Visual Similarity Assessment
IEEE Internet Computing
Designing ethical phishing experiments: a study of (ROT13) rOnl query features
Proceedings of the 15th international conference on World Wide Web
PHONEY: Mimicking User Response to Detect Phishing Attacks
WOWMOM '06 Proceedings of the 2006 International Symposium on on World of Wireless, Mobile and Multimedia Networks
Web wallet: preventing phishing attacks by revealing user intentions
SOUPS '06 Proceedings of the second symposium on Usable privacy and security
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Protecting people from phishing: the design and evaluation of an embedded training email system
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Cantina: a content-based approach to detecting phishing web sites
Proceedings of the 16th international conference on World Wide Web
Learning to detect phishing emails
Proceedings of the 16th international conference on World Wide Web
The Emperor's New Security Indicators
SP '07 Proceedings of the 2007 IEEE Symposium on Security and Privacy
Anti-Phishing Phil: the design and evaluation of a game that teaches people not to fall for phish
Proceedings of the 3rd symposium on Usable privacy and security
Multi-Level Reputation-Based Greylisting
ARES '08 Proceedings of the 2008 Third International Conference on Availability, Reliability and Security
SPS: a simple filtering algorithm to thwart phishing attacks
AINTEC'05 Proceedings of the First Asian Internet Engineering conference on Technologies for Advanced Heterogeneous Networks
Assessing the severity of phishing attacks: A hybrid data mining approach
Decision Support Systems
Proceedings of the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference
PCA document reconstruction for email classification
Computational Statistics & Data Analysis
Hybrid feature selection for phishing email detection
ICA3PP'11 Proceedings of the 11th international conference on Algorithms and architectures for parallel processing - Volume Part II
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We discuss a classification-based approach for filtering phishing messages in an e-mail stream. Upon arrival, various features of every e-mail are extracted. This forms the basis of a classification process which detects potentially harmful phishing messages. We introduce various new features for identifying phishing messages and rank established as well as newly introduced features according to their significance for this classification problem. Moreover, in contrast to classical binary classification approaches (spam vs. not spam), a more refined ternary classification approach for filtering e-mail data is investigated which automatically distinguishes three message types: ham (solicited e-mail), spam, and phishing. Experiments with representative data sets illustrate that our approach yields better classification results than existing phishing detection methods. Moreover, the direct ternary classification proposed is compared to a sequence of two binary classification processes. Direct one-step ternary classification is not only more efficient, but is also shown to achieve better accuracy than repeated binary classification.