Hybrid Ensemble Machine Learning Approach for URL Phishing Detection
Nikita Jagdale, Pallavi Vijay Chavan · 2022 2nd Asian Conference on Innovation in Technology (ASIANCON) · 2022
Phishing has been a massive issue on the Internet. It is a sort of Internet fraud. Thus, no antivirus or other type of technological security can eliminate this. But there has been research going on and executed from intellects across the globe to fight this online fraud. Researchers have also come up with additional strategies to deal with this problem. The two main focused techniques taken to tackle phishing are Black Listing and Machine Learning. Machine Learning is a fresh and ingenious way to tackle phishing. For this thesis, I went with the standard and Hybrid ensemble Machine Learning-based approach to dive phishing. This thesis consists of a comparative study of different Machine Learning algorithms like Decision Tree, Naive Bayes, Logistic Regression and Hybrid Ensemble Stacking algorithm in order to know the prediction that these standard machine Learning algorithms make. The results obtained by them show accuracy of 87.04%, 85.59%, 86.18% and 89.25% respectively.