Phishing attack has become very common in recent years especially in the financial application setting. A lot of losses have been recorded as a result of this attack from users and subscribers all over the globe. The research community has also been very concerned about this development with leaves an unsavoury aftermath on its victims, hence, several models, systems, architecture and frameworks have been developed by the researcher in an attempt to tackle this menace. The key limitations of these developments include lack of standard classification, low model efficiency and performance in terms of speed and time and high cost of developing the models. In this work, we have developed an enhanced Phishing detection system using Associative Classification technique. The Structured System Analysis and Design Methodology (SSADM) was adopted in this approach. The system was implemented using Hypertext Preprocessor (PHP) and MySQL as database. Form our results the proposed model had an overall accuracy score of 86.6% which outperformed the existing system with 55.9% when evaluated using selected parameters. This system could be beneficial to Nigerian Banks, to Digital Banking Application Users and to the entire research community.

Phishing Associative Classification Malware Identity Theft