Abstract: Phishing is an aspect of identity theft that uses engineering and social paradigms to steal personal information from unsuspecting users for their selfish gain. Most attacks are in the form of luring the user into clicking a link that directs the user to a rogue page. The major target of phishing attacks are online customers of e-banking and payment service providers, and these groups suffer huge financial loss. Phishing is not all about sending spoofed mails to users as most people assume, rather it is a multifaceted techno-social issue for which there is no particular solution to end its reign. This has given rise to a series of research in this field as scholars are working towards creating more efficient anti-phish solutions by quantifying risks and degree of vulnerability of users. Most approaches to combat phishing are not able to make dynamic decisions to determine the risk rate of the website and this allows for a large number of false positive. The use of blacklists and whitelists has their limitations due to poor scalability and time constraint. Anti-phishing solutions are methods put in place to protect internet users from attacks aimed at defrauding them of their finances. Browser plug-ins have been the most recent methods adopted, though a lot of questions have been raised to question the effectiveness of these plug-ins. In this research we aim at developing an intelligent anti-phishing plug-in for e-banking, capable of detecting phishing attacks based on existing knowledge about features and patterns of phishing websites. The proposed system is developed to protect users from deceptive tricks used by phishers by giving them the ability to identify phishy or fraudulent websites. The proposed model uses fuzzy logic to define rules and assign linguistic indicators in the form of if-then rules to each phishing criteria. The approach adopted is a combination of fuzzy reasoning in quantifying dynamic and unclear phishing.......................