Phishing URL detection-based feature selection to classifiers Online publication date: Sun, 30-Apr-2017
by S. Carolin Jeeva; Elijah Blessing Rajsingh
International Journal of Electronic Security and Digital Forensics (IJESDF), Vol. 9, No. 2, 2017
Abstract: Phishing is an online scandalous act that occurs when a malevolent web page impersonates as legitimate web page in the intension of exploiting the confidential information from the user. Phishing attack continues to pose serious risk for web users and annoying threat in the field of electronic commerce. Feature selection is the process of removing unrelated features and thus reduces the dimensionality of the feature. This paper focuses on identifying the foremost features that categorise legitimate websites from phishing websites based on feature selection. In real world identifying phishing URL with low computational time and accuracy is very important and thus feature selection is considered in this work. A comparative study is carried out on different data mining classifiers before and after feature selection and the performance are evaluated in terms of accuracy and computational rate. The results indicate that the proposed approach detects phishing websites with considerable accuracy.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Electronic Security and Digital Forensics (IJESDF):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook