A content and URL analysis‐based efficient approach to detect smishing SMS in intelligent systems. Issue 12 (6th September 2022)

• Record Type: Journal Article
• Authors: Jain, Ankit K.; Gupta, Brij B.; Kaur, Kamaljeet; Bhutani, Piyush; Alhalabi, Wadee; Almomani, Ammar
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 12(2022)
• Page Start: 11117
• Page End: 11141
• Publication Date: 2022-09-06
• Subjects: machine learning -- mobile phishing -- short message service -- smishing -- uniform resource locator; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.23035
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: Smishing is a combined form of short message service (SMS) and phishing in which a malicious text message or SMS is sent to mobile users. This form of attack has come to be a severe cyber‐security difficulty and has triggered incredible monetary losses to the victims. Many antismishing solutions for mobile devices have been proposed till date but still, there is a lack of a full‐fledged solution. Therefore, this paper proposes an efficient approach that analyzes text content and uniform resource locator (URL) presented in the SMS. We have integrated the URL phishing classifier with the text classifier to improve accuracy as some of the SMS contain the URL with no text or much less text. To find out rare words in a report, depending upon the frequency of term (TF) and the reciprocal of document frequency TF‐inverse document frequency (IDF), a weighting framework TF‐IDF is used. We have used two data sets for both text as well as for URL phishing classifier and used a synthetic minority oversampling technique to balance the training data. The voting classifier simply merges the findings of each classifier passed into it and predicts the output on the basis of voting. In proposed approach integrating KNN, RF, and ETC can detect smishing messages with a 99.03% accuracy and 98.94% precision rate which is relatively efficient compared with existing ones like SmiDCA model which has the given accuracy of 96.40% using Random Forest classifier in BFSA, Feature‐Based it has an accuracy of 98.74% and 94.20% true positive rate and Smishing Detector it shows an overall accuracy of 96.29%.