Phishing Emails Detection in Cyber Security

As digital communication becomes increasingly integral to personal and corporate activities,phishing attacks have emerged as a prevalent threat, ingeniously mimicking legitimatesources to illicitly acquire sensitive information. This research paper details the developmentof a sophisticated phishing detection application utilizing the DistilBERT-based model, finetuned on a diverse array of email datasets. The application significantly enhances theprecision of phishing detection mechanisms, adeptly reducing the incidence of successfulphishing attacks. Initial tests have demonstrated a precision rate of over 95% in detectingphishing emails, outperforming traditional rule-based filters substantially. The applicationexhibits robust defences against zero-day phishing attacks through its advanced machinelearning framework, which dynamically adapts to emerging phishing strategies. This paperexplores the methodology of developing the DistilBERT model, evaluates its efficacy againstexisting solutions, and discusses its implications for future cybersecurity practices. Thestudy’s findings underscore the potential of AI-driven tools in transforming cybersecuritymeasures, offering a proactive approach to thwarting phishing attempts and safeguardingsensitive data.