Researchers have developed an innovative browser extension that integrates machine learning algorithms to detect phishing websites with remarkable accuracy. This new technology addresses the limitations of traditional phishing detection methods, which often struggle to identify zero-day attacks. By leveraging machine learning, the extension can identify and block phishing attempts in real-time, providing enhanced protection against sophisticated threats that evade conventional security measures.
The extension’s development involved creating a machine learning model using Python and integrating it with a browser extension built with JavaScript, HTML, and CSS. The model, trained on datasets from PhishTank and Kaggle, uses the Random Forest algorithm, which outperformed other models in terms of accuracy and precision. The model achieved an impressive accuracy rate of 98.32% and demonstrated a 99.11% accuracy rate in detecting zero-day phishing URLs during testing.
The extension’s effectiveness was further validated by its ability to detect phishing URLs that eluded Google Safe Browsing. This high rate of correct predictions underscores the extension’s capability to address emerging phishing threats that traditional methods may miss. The inclusion of a reporting system allows users to flag suspicious websites, contributing to ongoing improvements in the model’s performance by incorporating new phishing trends and tactics.
Looking ahead, the researchers suggest future enhancements, such as dynamic dataset updates and advanced algorithms, to maintain the extension’s effectiveness against evolving phishing threats. The ultimate goal is to integrate this solution directly into web browsers, offering users seamless protection while browsing. This development marks a significant advancement in cybersecurity, setting a new standard for real-time phishing detection and user safety.
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