Machine Learning-Based Effective Malicious Web Page Detection

Abstract

The use of the Internet is becoming more and more widespread day by day, putting millions of users at risk of cyberattacks. Especially during the Covid-19 epidemic, internet usage has increased significantly and various cyber-attacks have been made through malicious websites. With these attacks, much information such as people’s private information, bank information, and social information can be captured. Many methods have been developed to prevent cyber-attacks. In particular, methods that use machine learning methods other than traditional methods give more successful results. In this study, it has been tried to automatically detect malicious websites by using the URL properties of malicious websites. For this purpose, popular machine learning methods such as DT, kNN, LightGBM, LR, MLP, RF, SVM, and XGBoost were used. According to the experimental results, the RF algorithm achieved 96% accuracy.

Keywords

• Malicious websites
• cyber attacks
• machine learning.

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