Abstract
We present a novel client-side In-browser phishing detection plugin that can automatically detect and warn users about phishing websites in real-time, using pattern matching based on the random forest classifier. Random Forest classifier is established in literature to perform considerably well in detecting phishing websites. The advantages of the proposed method include improved privacy of users' browsing data and performance in spite of low network latency. The system was implemented mainly, using JavaScript, while the Random Forest classifier was trained on the phishing websites dataset using python scikit-learn. The learned model parameters were exported in a portable format for the plug-in to be lightweight; this was in consideration of the expected low-latent processing power of the client machines. To the best of our knowledge, this is the first implementation of phishing website detection In-browser plugin without the use of external web services; the plugin with a one-time download of the learned model will be able to classify websites in real-time. Keywords— In-browser, Phishing, Random Forest Classifier, Low Latency, Server-side, Client-side, Real-time
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