Name
Abstract | ||
|---|---|---|
In this paper, a novel method for profiling phishing activity from an analysis of phishing emails is proposed. Profiling is useful in determining the activity of an individual or a particular group of phishers. Work in the area of phishing is usually aimed at detection of phishing emails. In this paper, we concentrate on profiling as distinct from detection of phishing emails. We formulate the profiling problem as a multi-label classification problem using the hyperlinks in the phishing emails as features and structural properties of emails along with who is (i.e. DNS) information on hyperlinks as profile classes. Further, we generate profiles based on classifier predictions. Thus, classes become elements of profiles. We employ a boosting algorithm (AdaBoost) as well as SVM to generate multi-label class predictions on three different datasets created from hyperlink information in phishing emails. These predictions are further utilized to generate complete profiles of these emails. Results show that profiling can be done with quite high accuracy using hyperlink information. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ASONAM.2010.56 | Advances in Social Networks Analysis and Mining |
Keywords | DocType | ISBN |
phishing emails,classifier prediction,multi-label classification problem,different datasets,multi-label class prediction,phishing activity,hyperlink information,complete profile,profiling problem,high accuracy,feature extraction,support vector machine,boosting algorithm,servers,adaboost,learning artificial intelligence,html,support vector machines,prediction algorithms | Conference | 978-0-7695-4138-9 |
Citations | PageRank | References |
10 | 0.63 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| John Yearwood | 1 | 117 | 16.21 |
| Musa Mammadov | 2 | 50 | 5.09 |
| Arunava Banerjee | 3 | 313 | 29.18 |