The nature of statistical learning theory
The nature of statistical learning theory
PALO: a probabilistic hill-climbing algorithm
Artificial Intelligence
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Anomaly Based Web Phishing Page Detection
ACSAC '06 Proceedings of the 22nd Annual Computer Security Applications Conference
Learning to detect phishing emails
Proceedings of the 16th international conference on World Wide Web
A distributed PSO-SVM hybrid system with feature selection and parameter optimization
Applied Soft Computing
Spamalytics: an empirical analysis of spam marketing conversion
Proceedings of the 15th ACM conference on Computer and communications security
A taxonomy and adversarial model for attacks against network log anonymization
Proceedings of the 2009 ACM symposium on Applied Computing
Reducing Features to Improve Bug Prediction
ASE '09 Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering
Impact of Feature Reduction on the Efficiency of Wireless Intrusion Detection Systems
IEEE Transactions on Parallel and Distributed Systems
Using GMDH-based networks for improved spam detection and email feature analysis
Applied Soft Computing
Profiling Phishing Emails Based on Hyperlink Information
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
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Phishing is a kind of embezzlement that uses social engineering in order to obtain personal information from its victims, aiming to cause losses. In the technical literature only the hit rate of the classifiers is mentioned to justify the effectiveness of the phishing detecting techniques. Aspects such as the accuracy of the classifier results (false positive rate), computational effort and the number of features used for phishing detection are rarely taken into account. In this work we propose a technique that yields the minimum set of relevant features providing reliability, good performance and flexibility to the phishing detection engine. The experimental results reported in this work show that the proposed technique could be used to optimize the detection engine of the anti-phishing scheme.