NDLI: Feature extraction process: A phishing detection approach

Content Provider	IEEE Xplore Digital Library
Author	Abunadi, A. Akanbi, O. Zainal, A.
Copyright Year	2013
Description	Author affiliation: Fac. of Comput., Univ. Teknol. Malaysia, Skudai, Malaysia (Abunadi, A.; Akanbi, O.; Zainal, A.)
Abstract	In order to circumvent the adverse effect of fraudulent acts committed on the internet by adversaries, different researchers have proposed various solution to this problem. One of this online fraudulent act is website phishing. Website phishing is the act of luring unsuspecting online users into divulging private and confidential information which can be used by the phisher in fraud, blackmail or other ways to negatively affect the users involved. In this paper, we propose noble features to better improve the accuracy of machine learning algorithms in classifying phish. Furthermore, ranking of these new features according to their weighted values with existing features is carried out in order to show the potency of the new feature as compared with the current features. The experimental result of the research shows that the new features are highly potent and can be used to enhance the better performance of machine learning algorithm used for phishing detection.
Starting Page	331
Ending Page	335
File Size	408846
Page Count	5
File Format	PDF
e-ISBN	9781479935161
DOI	10.1109/ISDA.2013.6920759
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-08
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine-Learning Google Online Users Bibliographies Phishing Classification Feature extraction Website Fraud Artificial intelligence
Content Type	Text
Resource Type	Article

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