Lightweight and Explainable Real-Time Detection of Social Media-Borne Phishing URLS: A Critical Review for Indian Context

Abstract: Cybercrime in India has undergone a fundamental transformation in the last five years. Phishing, which was once limited to fraudulent emails, has now shifted aggressively to social media platforms such as WhatsApp, Telegram, Instagram and Facebook. Cybercriminals exploit trust by circulating deceptive links that promise free mobile recharge, government jobs, scholarship schemes, KYC updates and lottery prizes. Academic researchers have responded with a large number of machine learning based detection systems that report very high accuracy, often exceeding 98 percent. However, these systems are developed in isolation from real-world deployment constraints. Critical practical metrics such as inference latency, model file size, throughput and explainability for non-technical users are completely ignored. Most models depend on external services such as WHOIS lookup, DNS query and PageRank fetching, which require network calls and introduce significant delay. This paper presents a comprehensive critical theoretical review of six prominent recent studies published between 2025 and 2026.

Keywords: Phishing Detection, Social Media Security, Lightweight ML, Explainable AI, Real-Time Detection