Detecting phishing e-mails using text and data mining

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{Pandey:2012:ICCIC,

• author = "Mayank Pandey and Vadlamani Ravi",
• booktitle = "Computational Intelligence Computing Research (ICCIC), 2012 IEEE International Conference on",
• title = "Detecting phishing e-mails using text and data mining",
• year = "2012",
• DOI = "doi:10.1109/ICCIC.2012.6510259",
• abstract = "This paper presents text and data mining in tandem to detect the phishing email. The study employs Multilayer Perceptron (MLP), Decision Trees (DT), Support Vector Machine (SVM), Group Method of Data Handling (GMDH), Probabilistic Neural Net (PNN), Genetic Programming (GP) and Logistic Regression (LR) for classification. A dataset of 2500 phishing and non phishing emails is analysed after extracting 23 keywords from the email bodies using text mining from the original dataset. Further, we selected 12 most important features using t-statistic based feature selection. Here, we did not find statistically significant difference in sensitivity as indicated by t-test at 1percent level of significance, both with and without feature selection across all techniques except PNN. Since, the GP and DT are not statistically significantly different either with or without feature selection at 1percent level of significance, DT should be preferred because it yields 'if-then' rules, thereby increasing the comprehensibility of the system.",
• keywords = "genetic algorithms, genetic programming, Classification, Decision Tree, Group Method Of Data Handling, Logistic regression, Multilayer Perceptron, Phishing webpage, Probabilistic Neural Network, Support Vector Machine, Text mining",
• notes = "Also known as \cite{6510259}",

}

Genetic Programming entries for Mayank Pandey Vadlamani Ravi

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