An introduction to variable and feature selection
The Journal of Machine Learning Research
Survey of Text Mining
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
Learning to detect phishing emails
Proceedings of the 16th international conference on World Wide Web
A comparison of machine learning techniques for phishing detection
Proceedings of the anti-phishing working groups 2nd annual eCrime researchers summit
Bayesian Additive Regression Trees-Based Spam Detection for Enhanced Email Privacy
ARES '08 Proceedings of the 2008 Third International Conference on Availability, Reliability and Security
A quantitative approach to estimate a website security risk using whitelist
Security and Communication Networks
Towards preventing QR code based attacks on android phone using security warnings
Proceedings of the 8th ACM SIGSAC symposium on Information, computer and communications security
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Phishing continue to be one of the most drastic attacks causing both financial institutions and customers huge monetary losses. Nowadays mobile devices are widely used to access the Internet and therefore access financial and confidential data. However, unlike PCs and wired devices, such devices lack basic defensive applications to protect against various types of attacks. In consequence, phishing has evolved to target mobile users in Vishing and SMishing attacks recently. This study presents a client-server distributed architecture to detect phishing e-mails by taking advantage of automatic variable selection in Bayesian Additive Regression Trees (BART). When combined with other classifiers, BART improves their predictive accuracy. Further the overall architecture proves to leverage well in resource constrained environments.