- Title
- Profiling phishing activity based on hyperlinks extracted from phishing emails
- Creator
- Yearwood, John; Mammadov, Musa; Webb, Dean
- Date
- 2012
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/163038
- Identifier
- vital:12761
- Identifier
-
https://doi.org/10.1007/s13278-011-0031-y
- Identifier
- ISBN:1869-5450
- Abstract
- Phishing activity has recently been focused on social networking sites as a more effective way of exploiting not only the technology but also the trust that may exist between members in a social network. 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 whois (i.e. DNS) information on hyperlinks as profile classes. Further, we generate profiles based on the 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.
- Relation
- Social Network Analysis and Mining Vol. 2, no. 1 (2012), p. 5-16
- Rights
- Copyright Springer
- Rights
- This metadata is freely available under a CCO license
- Subject
- Phishing; Profiling phishing emails; Multi-label classification; 0199 Other Mathematical Sciences; 0806 Information Systems; 0899 Other Information and Computing Sciences
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