Intelligent phishing detection and protection scheme for online transactions

• Published in: Expert systems with applications 2013-09, Vol.40 (11), p.4697-4706
• Main Authors: Barraclough, P.A., Hossain, M.A., Tahir, M.A., Sexton, G., Aslam, N.
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial intelligence; Biological and medical sciences; Computer information security; Computer science; control theory; systems; Connectionism. Neural networks; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; Legitimate site rules; Locators; Memory and file management (including protection and security); Memory organisation. Data processing; Neuro-Fuzzy scheme; On-line systems; Online; Online transaction; Organizations; Phishing; Psychology. Psychoanalysis. Psychiatry; Psychology. Psychophysiology; Social interactions. Communication. Group processes; Social psychology; Software; Training; Websites
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2013.02.009

► Explored the existing phishing detection and protection scheme. ► Identified more the legitimate rules and criteria for proposed scheme. ► Proposed a new Neuro-Fuzzy scheme that offered best performance, so far. Phishing is an instance of social engineering techniques used to deceive users into giving their sensitive information using an illegitimate website that looks and feels exactly like the target organization website. Most phishing detection approaches utilizes Uniform Resource Locator (URL) blacklists or phishing website features combined with machine learning techniques to combat phishing. Despite the existing approaches that utilize URL blacklists, they cannot generalize well with new phishing attacks due to human weakness in verifying blacklists, while the existing feature-based methods suffer high false positive rates and insufficient phishing features. As a result, this leads to an inadequacy in the online transactions. To solve this problem robustly, the proposed study introduces new inputs (Legitimate site rules, User-behavior profile, PhishTank, User-specific sites, Pop-Ups from emails) which were not considered previously in a single protection platform. The idea is to utilize a Neuro-Fuzzy Scheme with 5 inputs to detect phishing sites with high accuracy in real-time. In this study, 2-Fold cross-validation is applied for training and testing the proposed model. A total of 288 features with 5 inputs were used and has so far achieved the best performance as compared to all previously reported results in the field.