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The internet has become an important component of our daily lives. The most common internet service is web surfing. Many individuals use their browser to do things like online shopping, bill payment, cell phone recharge, and banking transactions. Customers confront different security dangers, such as cyber crime, as a result of the widespread usage of this service. Cyber phishing is a type of web threat that entices users to connect with a false website. The main goal of this research paper is to prevent the user's sensitive information. The proposed model is developed in three steps in step1 we choose a dataset to train on, and then test classifiers on the dataset. In step2 we have applied the three classifiers finding phishing detection accuracy and finally step3 after completing all of the predictions, we discovered that XGBoost outperformed AdaBoost and Gradient boosting machine learning algorithms. |
