Title: Scalable malicious URL detection technique for smishing attacks

Authors: Razvan Stoleriu; Catalin Negru; Bogdan-Costel Mocanu; Emil-Andrei Constantinescu; Alexandra-Elena Mocanu; Florin Pop

Addresses: National University of Science and Technology Politehnica, Bucharest, Romania ' National University of Science and Technology Politehnica, Bucharest, Romania ' National University of Science and Technology Politehnica, Bucharest, Romania ' Malware Analysis Department, National Cyberint Center, Bucharest, Romania ' National University of Science and Technology Politehnica, Bucharest, Romania ' National University of Science and Technology Politehnica, Bucharest, Romania; National Institute for Research and Development in Informatics – ICI, Bucharest, Romania; Academy of Romanian Scientists, Bucharest, Romania

Abstract: Nowadays, smartphones are used daily and use sensitive information making people more vulnerable to cyber-security attacks. The easiest way for attackers to access a smartphone is through SMS phishing (smishing) using URL shortening services. In this paper, we propose a scalable technique to detect malicious URLs in smishing attacks based on a cloud-edge architecture, using threat intelligence platforms (e.g., VirusTotal and PhishTank), and machine learning algorithms that classify the URLs based on their features. We used a public dataset for training and proposed new features to improve it. We evaluated our proposed ML model against JRip, PART, J48, and random forest algorithms. Our model has improved performance compared to similar solutions, obtaining an accuracy of approximately 97%. To showcase the effectiveness of our solution, we implement an Android application that detects malicious short URLs in SMS messages and notifies the user concerning their legitimacy.

Keywords: smishing attacks; malicious URLs; edge-cloud computing; threat intelligence; machine learning.

DOI: 10.1504/IJCSE.2025.147610

International Journal of Computational Science and Engineering, 2025 Vol.28 No.4, pp.419 - 433

Received: 13 Oct 2023
Accepted: 31 Jan 2024
Published online: 24 Jul 2025 *

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