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Vol.4 , No. 4, Publication Date: Sep. 26, 2017, Page: 31-35
 representation to find the best level of detailed features that helps in capturing the phishing properties. Moreover, such hierarchical representation is used to capture strength and weakness of the classifier itself and other classifiers that are used for a comparative study. The proposed method has a fair accuracy as compared to well-known algorithms. For the tested data set, its obtained accuracy was close to that obtained by KNN and SVM. The proposed classier, for the conducted experiments, is better than other classifiers that are rule based. The cAnt-MinerPB has shown promising results compared to the well-known and well-established classification techniques.&picture=http://www.aascit.org/aascit/decorators/img/journal/912.jpg)
| [1] | Asma Al Sarhan, Computers Sciences Department, KASIT, University of Jordan, Amman, Jordan. |
| [2] | Riad Jabri, Computers Sciences Department, KASIT, University of Jordan, Amman, Jordan. |
| [3] | Ahmad Sharieh, Computers Sciences Department, KASIT, University of Jordan, Amman, Jordan. |
Phishing is stealing users' confidential information by uploading a fake website that claims to be of another. Such a web site contains special features that aid an automatic classification of it as phishing one. However, there is no single feature that to be used to identify the phishing web-sites. Subsequently, the properties of phishing website are defined as a collection of features and then used to actively discover such sites in real-time. This research develops automatic classification of a web-site into a phishing or non-phishing one based on aggregation of a set of predetermined features related to the content of the site. A classifier is developed based on Ant-Colony optimization, known as cAnt-MinerPB. The features are combined and listed in a tree structure using document object model (DOM) representation to find the best level of detailed features that helps in capturing the phishing properties. Moreover, such hierarchical representation is used to capture strength and weakness of the classifier itself and other classifiers that are used for a comparative study. The proposed method has a fair accuracy as compared to well-known algorithms. For the tested data set, its obtained accuracy was close to that obtained by KNN and SVM. The proposed classier, for the conducted experiments, is better than other classifiers that are rule based. The cAnt-MinerPB has shown promising results compared to the well-known and well-established classification techniques.
Keywords
DOM-Tree Structure, cAnt-MinerPB Algorithm, Phishing Detection
Reference
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