AI-Based Phishing Website Detection

Phishing websites imitate legitimate websites to deceive users into revealing sensitive information such as login credentials,banking details, and personal data. Traditional detection methods often depend on manually maintained blacklists, which maynot identify newly created or rapidly changing malicious websites. This paper presents a proposed AI-based phishing websitedetection system that analyzes URL characteristics and selected website signals to classify a submitted website as potentiallylegitimate or suspicious. The system extracts features such as URL length, use of an IP address, unusual symbols,domain-related indicators, HTTPS presence, and selected page-level signals where safely available. These features can beprocessed by a supervised machine-learning classifier and combined with transparent rule-based checks. The output is presentedas a risk label with a brief explanation, while uncertain cases are treated cautiously. The proposed design aims to support saferbrowsing and provide a modular foundation for further evaluation. The document describes a research-paper sample; actualdataset details, model selection, experimental results, and references must be verified against the author's implementation beforepublication.