Authors - Kalva Yamini, Kapilesh C, Hari Kishore R, Giri Karthick S Abstract - Phishing attacks remain among the most prevalent cybersecurity threats, exploiting deceptive URLs that imitate legitimate domains. Traditional blacklist and heuristic-based methods fail to detect zero-day phishing URLs, leaving users exposed to novel attack vectors. This paper presents 3P-VAD (Three-Phase Verification and Detection), an AI-powered system for real-time URL classification integrating three complementary layers: (i) threat intelligence dataset lookup against live feeds, (ii) multi-engine verification via the VirusTotal API aggregating results from 70+ security vendors, and (iii) a Convolutional Neural Network (CNN)-based zero-day detection model operating exclusively on URL character sequences. A selection-based scanning mechanism enables on-demand URL verification, enhancing user privacy by preventing inadvertent submission of sensitive internal URLs to third-party services. Evaluated on 2 million URLs, the framework achieved 95.0% accuracy, 94.5% precision, 86.0% recall, and 90.0% F1-score on the CNN zero-day component, with 100% combined detection rate across all three phases. Ablation experiments confirm non-redundant, complementary coverage.
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