Intelligent cyber-phishing detection for online. Issue 104 (May 2021)

• Record Type: Journal Article
• Authors: Barraclough, P.A.; Fehringer, G.; Woodward, J.
• Is Part Of: Computers & security. Issue 104(2021)
• Publication Date: 2021-05
• Subjects: Phishing -- Cyber-phishing -- Intelligent -- ANFIS -- FIS -- Fuzzy systems; Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.cose.2020.102123
• Languages: English
• ISSNs: 0167-4048

Abstract

Highlights: A combined blacklist-based, web content-based and heuristic approaches using multiple algorithms with features to detect fraudulent sites with higher accuracy in real-time. The methodology can reduce fraudulent attacks and protect online users. The integrated method and four categories of dataset are the core framework from which we extract comprehensive features. Abstract: Phishing attacks are on the increase, resulting in financial loss and theft of sensitive information to online services and users. Anti-phishing approaches have concentrated on blacklist-based approaches that use manually verified Unified Resource Locators (URLs); or content-based methods that utilise heuristics-based machine learning (ML) classifiers. However, online deception is still on the rise. In this study, we introduce a novel methodology combining blacklist-based, web content-based and heuristic based approaches, using ML algorithms with comprehensive features to allow more accurate phishing attack detection. Extensive evaluation was carried out based on Adaptive neuro-fuzzy inference system (ANFIS), Naïve Bayes (NB), PART, J48, and JRip with features, using evaluation methods (metrics) to measure the proposed method performance. All the classifiers achieved over 99% - 99.33% accuracy. PART attained 99.33% accuracy with 0.006 seconds (secs) speed, which is the best performance. We experimentally demonstrate that the proposed methodology can detect phishing websites with a high accuracy in real-time and generalise well to new phishing attacks. The proposed approach has the best performance compared to related approaches in the field.