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The SVM Based Interactive tool for Predicting Phishing Websites

Author(s): Santhana Lakshmi V | Vijaya MS

Journal: International Journal of Computer Science and Information Security
ISSN 1947-5500

Volume: 9;
Issue: 10;
Start page: 58;
Date: 2011;
Original page

Keywords: Antiphishing | Blacklist | Classification | Machine Learning | Phishing | Prediction

Phishing is a form of social engineering in which attackers endeavor to fraudulently retrieve the legitimate user’s confidential or sensitive credentials by imitating electronic communications from a trustworthy or public organization in an automated fashion. Such communications are done through email or deceitful website that in turn collects the credentials without the knowledge of the users. Phishing website is a mock website whose look and feel is almost identical to the legitimate website. So internet users expose their data expecting that these websites come from trusted financial institutions. Several antiphishing methods have been introduced to prevent people from becoming a victim to these types of phishing attacks. Regardless of the efforts taken, the phishing attacks are not alleviated. Hence it is more essential to detect the phishing websites in order to preserve the valuable data. This paper demonstrates the modeling of phishing website detection problem as binary classification task and provides convenient solution based on support vector machine, a pattern classification algorithm. The phishing website detection model is generated by learning the features that have been extracted from phishing and legitimate websites. A third party service called ‘blacklist’ is used as one of the feature that helps to envisage the phishing website effectively. Various experiments have been carried out and the performance analysis shows that the SVM based model outperforms well.
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