Phishing Detection Using Hybrid Machine learning Techniques
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Cyber security has become a crucial component of the new digital age with more than 820 million users of internet in year 2023 and social media users are expected to reach 82.3% from the total number of internet users by 2024. According to these figures, security systems are required to shield the public from phishing scams, which have a negative impact not only on financial resources but also on people's mental health by making them fearful to use the internet or surf. This drives efforts to find effective solutions for the issue. The swift alterations in phishing attack patterns necessitate constant improvement of existing phishing detection systems in order to effectively counter new and upcoming phishing attempts.
This research aims to identify common characteristics displayed by phishing websites and create a model to identify them. The dataset was used to train a number of models, including the Random Forest Classifier, Artificial Neural Networks, and Principal component Analysis. Feature selection and clustering technique were also integrated to detect unknown attacks. The dataset was collected from Kaggle and contains information of 549,346 entries. RF attained the highest accuracy of 94%.
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