A content and URL analysis‐based efficient approach to detect smishing SMS in intelligent systems

International Journal of Intelligent SystemsVolume 37, Issue 12 p. 11117-11141 RESEARCH ARTICLE A content and URL analysis-based efficient approach to detect smishing SMS in intelligent systems Ankit K. Jain, Ankit K. Jain Department of Computer Engineering, National Institute of Technology, Kurukshetra, IndiaSearch for more papers by this authorBrij B. Gupta, Corresponding Author Brij B. Gupta [email protected] International Center for AI and Cyber Security Research and Innovations & Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan Department of Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia Research and Innovation Department, Skyline University College, Sharjah, UAE Lebanese American University, Beirut, Lebanon Correspondence Brij B. Gupta, International Center for AI and Cyber Security Research and Innovations & Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan Email: [email protected]Search for more papers by this authorKamaljeet Kaur, Kamaljeet Kaur Department of Computer Engineering, National Institute of Technology, Kurukshetra, IndiaSearch for more papers by this authorPiyush Bhutani, Piyush Bhutani Department of Computer Engineering, National Institute of Technology, Kurukshetra, IndiaSearch for more papers by this authorWadee Alhalabi, Wadee Alhalabi Department of Computer Science, King Abdulaziz University, Jeddah, Saudi ArabiaSearch for more papers by this authorAmmar Almomani, Ammar Almomani Research and Innovation Department, Skyline University College, Sharjah, UAE IT-department-Al-Huson University College, Al-Balqa Applied University, Irbid, JordanSearch for more papers by this author Ankit K. Jain, Ankit K. Jain Department of Computer Engineering, National Institute of Technology, Kurukshetra, IndiaSearch for more papers by this authorBrij B. Gupta, Corresponding Author Brij B. Gupta [email protected] International Center for AI and Cyber Security Research and Innovations & Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan Department of Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia Research and Innovation Department, Skyline University College, Sharjah, UAE Lebanese American University, Beirut, Lebanon Correspondence Brij B. Gupta, International Center for AI and Cyber Security Research and Innovations & Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan Email: [email protected]Search for more papers by this authorKamaljeet Kaur, Kamaljeet Kaur Department of Computer Engineering, National Institute of Technology, Kurukshetra, IndiaSearch for more papers by this authorPiyush Bhutani, Piyush Bhutani Department of Computer Engineering, National Institute of Technology, Kurukshetra, IndiaSearch for more papers by this authorWadee Alhalabi, Wadee Alhalabi Department of Computer Science, King Abdulaziz University, Jeddah, Saudi ArabiaSearch for more papers by this authorAmmar Almomani, Ammar Almomani Research and Innovation Department, Skyline University College, Sharjah, UAE IT-department-Al-Huson University College, Al-Balqa Applied University, Irbid, JordanSearch for more papers by this author First published: 06 September 2022 https://doi.org/10.1002/int.23035Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat 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%. CONFLICT OF INTEREST The authors declare no conflict of interest. Open Research DATA AVAILABILITY STATEMENT Data will be made available on the request. REFERENCES 1Jain AK, Gupta BB. 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Citing Literature Volume37, Issue12December 2022Pages 11117-11141 ReferencesRelatedInformation