Name
Abstract | ||
|---|---|---|
PurposeThe aim of this study is to propose an efficient rule extraction and integration approach for identifying phishing websites. The proposed approach can elucidate patterns of phishing websites and identify them accurately.Design/methodology/approachHyperlink indicators along with URL-based features are used to build the identification model. In the proposed approach, very simple rules are first extracted based on individual features to provide meaningful and easy-to-understand rules. Then, the F-measure score is used to select high-quality rules for identifying phishing websites. To construct a reliable and promising phishing website identification model, the selected rules are integrated using a simple neural network model.FindingsExperiments conducted using self-collected and benchmark data sets show that the proposed approach outperforms 16 commonly used classifiers (including seven non-rule-based and four rule-based classifiers as well as five deep learning models) in terms of interpretability and identification performance.Originality/valueInvestigating patterns of phishing websites based on hyperlink indicators using the efficient rule-based approach is innovative. It is not only helpful for identifying phishing websites, but also beneficial for extracting simple and understandable rules. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1108/EL-01-2020-0016 | ELECTRONIC LIBRARY |
Keywords | DocType | Volume |
Phishing websites, Classification, Rule extraction, Hyperlink analysis, Neural networks | Journal | 38 |
Issue | ISSN | Citations |
5-6 | 0264-0473 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chaoqun Wang | 1 | 8 | 4.84 |
| Zhongyi Hu | 2 | 138 | 10.24 |
| Raymond Chiong | 3 | 349 | 41.79 |
| Yukun Bao | 4 | 342 | 29.68 |
| Jiang Wu | 5 | 19 | 6.17 |