Scalable malicious URL detection technique for smishing attacks
Răzvan Stoleriu, Cătălin Negru, Bogdan Mocanu, Emil Andrei Constantinescu, Alexandra-Elena Mocanu, Florin Pop · International Journal of Computational Science and Engineering · 2025
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.