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

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Abstract

With the increasing rate and catastrophic consequences of phishing attacks, research on antiphishing 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 a
Original languageEnglish
JournalComputer Journal
StatePublished - 2017

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