SmiDCA: An Anti-Smishing Model with Machine Learning Approach. (25th April 2018)

• Record Type: Journal Article
• Authors: Sonowal, Gunikhan; Kuppusamy, K S
• Is Part Of: Computer journal. Volume 61:Number 8(2018)
• Page Start: 1143
• Page End: 1157
• Publication Date: 2018-04-25
• Subjects: phishing -- smishing -- machine learning classifiers -- text mining -- Pearson correlation coefficient -- readability algorithms -- Part-of-Speech (POS); Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxy039
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: Phishing has become a serious cyber-security issue, and it is spreading through various media such as e-mail, SMS to capture the victim's critical profile information. Although many novel anti-phishing techniques have been developed to forestall the progress of phishing, it remains an unresolved issue. Smishing is an incarnation of Phishing attack, which utilizes Short Messaging Service (SMS) or simple text message on mobile phones to lure the victim's online credentials. This paper presents an anti-phishing model entitled 'SmiDCA' (SMIshing Detection based on Correlation Algorithm). The proposed model has collected different smishing messages from various sources, and 39 distinct features were extracted initially. The SmiDCA model incorporates dimensionality reduction, and machine Learning-based experiments were conducted on without (BFSA) and with (AFSA) reduction of features. The model has been validated with experiments on both the English and non-English datasets and the results of both of these experiments are encouraging in terms of accuracy: 96.40% for English dataset and 90.33% for the non-English dataset. In addition, the model achieved an accuracy of 96.16% even after nearly half of the features were pruned.