Abstract
Phishing attacks have become the preferred vehicle to gather sensitive information as well as to deliver dangerous malware. So far, there is still no phishing detection system that can perfectly detect and progressively self adapt to differentiate between phishing and legitimate websites. This paper proposes the case-based reasoning Phishing detection system (CBR-PDS) that relies on previous cases to detect phishing attacks. CBR-PDS is highly adaptive and dynamic as it can adapt to detect new phishing attacks using rather a small dataset size in contrast to other machine learning techniques. CBR-PDS aims to improve the detection accuracy and the reliability of the results by identifying a set of discriminative features and discarding irrelevant features. CBR-PDS relies on a two stage hybrid procedure using Information gain and Genetic algorithms. The reduction of the data dimensionality amounts to an improved accuracy rate, yet it necessitates a reduced processing time. The CBR-PDS is tested using different scenarios on a various balanced datasets. The obtained performances clearly show the suitability of our proposed hybrid feature selection procedure as well as the efficiency of the proposed CBR-PDS system. The obtained accuracy rates exceed 95%. We also show that the integration of an Online Phishing Threats component into the CBR-PDS system improves further the accuracy rate. Finally, CRB-PDS performances are compared to those of several known competitive classifiers.