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A comparison of machine learning techniques for phishing detection
| Content Provider | ACM Digital Library |
|---|---|
| Author | Nappa, Dario Wang, Xinlei Abu-Nimeh, Saeed Nair, Suku |
| 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. |
| Starting Page | 60 |
| Ending Page | 69 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781595939395 |
| DOI | 10.1145/1299015.1299021 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2007-10-04 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Logistic regression Phishing Classification Machine learning Nnet Svm Bart Random forests Cart |
| Content Type | Text |
| Resource Type | Article |
