Detection of Phishing Websites Based on Probabilistic Neural Networks and K-Medoids Clustering

With the increasing rate and catastrophic consequences of phishing attacks, research on anti-phishing solutions has gained growing importance in information security. Security risks may include information leakage, identity theft, financial loss and reputation sabotage. Raising human awareness is not a sufficient mitigation method and deploying complementary technical solutions is a crucial requirement. Although various approaches have been proposed in the literature, the design of efficient phishing detection models is a challenging task and the problem still lacks a complete solution. In this paper, we present a novel approach for detecting phishing websites based on probabilistic neural networks (PNNs). We also investigate the integration of PNN with K-medoids clustering to significantly reduce complexity without jeopardizing the detection accuracy. To assess the feasibility of the proposed approach, we conducted in-depth study to evaluate various performance measures on a publicly available data set composed of 11 055 phishing and benign websites. The experimental results show that more accurate models can be built and even with >40% reduction in the complexity, >97% accuracy can be achieved with low false errors.