GitHub / hasansust32 / Phishing_Detection_Using_Machine_Learning

This is a completely machine-learning based task. We used a dataset from kaggle with 1154 website details with 32 features. More significantly, we experimented with a considerable number of machine learningmethods on actual phishing datasets and against various criteria. We identify phishing websites using six distinct machine learningclassification methods. This research obtained a maximumachievable accuracy rate of 97.17 percent for the Random Forestrule and 94.75 percent for the Gradient Boost Classifier. The Provisioningaccuracy is 94.69 percent with the Decision Tree classifier, 92.76 percent with Logistic Regression, 60.45 percent with KNN, and 56.04 percent with SVM.

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Topics: classification-model, machine-learning, phishing, phishing-attacks, phishing-detection, security-vulnerability

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