Associative Classification Mining for Website Phishing Classification

摘要

Website phishing is one of the crucial research topics for the internet community due to the massive number of online daily transactions. The process of predicting the phishing activity for a website is a typical classification problem in data mining where different website's features such as URL length, prefix and suffix, IP address, etc., are used to discover concealed correlations (knowledge) among these features that are useful for decision makers. In this article, an Associative classification (AC) data mining algorithm that uses association rule methods to build classification systems (classifiers) is developed and applied on the important problem of phishing classification. The proposed algorithm employs a classifier building method that discovers vital rules that possibly can be utilised to detect phishing activity based on a number of significant website's features. Experimental results using the proposed algorithms and three other rule based algorithms on real legitimate and fake websites collected from different sources have been conducted. The results reveal that our algorithm is highly competitive in classifying websites if contrasted with the other rule based classification algorithms with respective to accuracy rate. Further, our algorithm normally extracts smaller classifiers than other AC algorithm because of its novel rule evaluation method which reduces overfitting.