A Comparative Analysis of Machine Learning Algorithms on Malicious URL Prediction
D. Vaishnavi, S. Suwetha, Y. Bevish Jinila, R. Subhashini, S. Prayla Shyry · 2021
Phishing is a form of fraudulent behavior where an entity or a person mimics to be a valid user. Phishing has become popular in cyber space and it is used as a tool for deceiving the users. Most of the phishing messages are difficult to interpret. In the existing literature, there are many schemes have addressed the phishing issue. Yet, there is no concrete solution to thwart such attacks. Taking this into consideration, to detect phishing threats, a machine learning-based prediction scheme is proposed in this article. From the experimental analysis, it is identified that logistic regression outperforms the other schemes in terms of accuracy and error rate. The accuracy on URL prediction with logistic regression is 97%.