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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
