The weighted majority algorithm
Information and Computation
A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Canning Spam: Proposed Solutions to Unwanted Email
IEEE Security and Privacy
Combining email models for false positive reduction
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Learning to detect phishing emails
Proceedings of the 16th international conference on World Wide Web
Assessing the severity of phishing attacks: A hybrid data mining approach
Decision Support Systems
Classification ensemble by genetic algorithms
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
A new adaptive framework for classifier ensemble in multiclass large data
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part I
A heuristic classifier ensemble for huge datasets
AMT'11 Proceedings of the 7th international conference on Active media technology
A scalable heuristic classifier for huge datasets: a theoretical approach
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Clustering potential phishing websites using DeepMD5
LEET'12 Proceedings of the 5th USENIX conference on Large-Scale Exploits and Emergent Threats
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Phishing attack is a kind of identity theft which tries to steal ?confidential data like on?-?line bank account information?. In a ?phishing attack scenario, attacker deceives users by a fake email ?which is called scam. In this paper we employ three different ?learning methods to detect phishing scams. Then, we use ?ensemble methods on their results to improve our scam ?detection mechanism. Experimental results show that ?the proposed method can detect 94.4% of scam emails ?correctly, while only 0.08% of legitimate emails are ?classified as scams.