Name
Abstract | ||
|---|---|---|
There are many applications available for phishing detection. However, unlike predicting spam, there are only few studies that compare machine learning techniques in predicting phishing. The present study compares the predictive accuracy of several machine learning methods including Logistic Regression (LR), Classification and Regression Trees (CART), Bayesian Additive Regression Trees (BART), Support Vector Machines (SVM), Random Forests (RF), and Neural Networks (NNet) for predicting phishing emails. A data set of 2889 phishing and legitimate emails is used in the comparative study. In addition, 43 features are used to train and test the classifiers. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1299015.1299021 | eCrime Researchers Summit |
Keywords | Field | DocType |
phishing emails,comparative study,neural networks,bayesian additive regression trees,present study,logistic regression,random forests,regression trees,legitimate emails,phishing detection,classification,phishing,regression tree,random forest,machine learning,neural network,svm | Data mining,Phishing,Regression,Computer science,Support vector machine,Artificial intelligence,Artificial neural network,Random forest,Logistic regression,Machine learning,Phishing detection,Bayesian probability | Conference |
Citations | PageRank | References |
111 | 5.38 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saeed Abu-Nimeh | 1 | 303 | 16.70 |
| Dario Nappa | 2 | 120 | 5.95 |
| Xinlei Wang | 3 | 228 | 16.47 |
| Suku Nair | 4 | 140 | 12.00 |