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Many classifications techniques have been used and devised to combat phishing threats, but none of them is able to efficiently identify web phishing attacks due to the continuous change and the short life cycle of phishing websites. In this paper, we introduce a Case-Based Reasoning (CBR) Phishing Detection System (CBR-PDS). It mainly depends on CBR methodology as a core part. The proposed system is highly adaptive and dynamic as it can easily adapt to detect new phishing attacks with a relatively small data set in contrast to other classifiers that need to be heavily trained in advance. We test our system using different scenarios on a balanced 572 phishing and legitimate URLs. Experiments show that the CBR-PDS system accuracy exceeds 95.62%, yet it significantly enhances the classification accuracy with a small set of features and limited data sets.
Abutair et al. (Sun,) studied this question.