Web phishing detection: feature selection using rough sets and ant colony optimisation. (2018)

• Record Type: Journal Article
• Authors: Penmatsa, Ravi Kiran Varma; Kakarlapudi, Padmaprabha
• Is Part Of: International journal of intelligent systems design and computing. Volume 2:Number 2(2018)
• Page Start: 102
• Page End: 113
• Publication Date: 2018
• Subjects: phishing -- web phishing detection -- feature selection -- rough sets -- RSs -- ant colony optimisation -- ACO -- classification accuracy -- feature reduction; Artificial intelligence -- Periodicals; Human-computer interaction -- Periodicals; 006.3
• Languages: English
• ISSNs: 2052-8477

Abstract

Phishing has become a global issue which is doing fraud by stealing online data. Because of phishing, many users may lose trust in online services which cause a negative effect on organisations. Predictive, preventive and counteractive measures taken for phishing is a crucial step towards protecting online business transactions. The accuracy of classifying any website as phished necessarily depends on the goodness of features selected. Using feature selection algorithms combined with optimisation techniques, appropriate features can be identified. Removal of a feature should not affect the accuracy of classification. This paper proposes rough-set and ant colony optimisation technique for attribute minimisation on standardised phishing dataset. Experiment results show improvement in performance with reduced attributes for web phishing detection.