AI-Oriented Phishing Detection System for the Strengthening of Security in Social Networks - Kumaraguru, V.; Mohanaprabanjan, B.; Prasanth, B.; Akilan, P.; Lingeshwaran, V. (Journal of Neonatal Surgery, 2025)
Phishing attacks on mobile users through messaging applications and social media are increasing in severity and have forced the need for proactive and automated detection methods. This paper presents a Mobile Phishing Link Detection System that uses machine learning and external threat intelligence on suspicious URLs received by notifications from emails, messaging apps, and social media (WhatsApp, Instagram, and Facebook). The detection system uses an ONNX-based neural network trained for mobile inference, a MongoDB database for fast local phishing link investigation, and the VirusTotal API for conditional external verification. The user is alerted and notified in real-time via foreground service notifications, and does not need to interact with the app to receive a notification. Evaluating the performance of the system resulted in evidence of the final ONNX model post-processing level having a precision of 94.6 percent and an F1-score of 93.1 percent. The latency tests showed a response time of 30 ms, 50 ms, and 500 ms when using the MongoDB database, model, and VirusTotal API, respectively. The system provides an efficient, scalable, identity-preserving solution for real-time detection of mobile phishing because it aims to provide strong protection against the occurrence of zero-day threats and increase the level of user security in an ever-changing mobile environment.