Detection of Phishing Websites Based on Probabilistic Neural Networks and K-Medoids Clustering. (18th April 2017)

• Record Type: Journal Article
• Authors: El-Alfy, El-Sayed M
• Is Part Of: Computer journal. Volume 60:Number 12(2017)
• Page Start: 1745
• Page End: 1759
• Publication Date: 2017-04-18
• Subjects: information and network security -- phishing attacks -- social engineering -- hybrid machine learning -- probabilistic neural networks -- clustering -- security analytics; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxx035
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: 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.