Malicious Website Detection Using Random Forest and Pearson Correlation for Effective Feature Selection

In recent years, the internet has expanded rapidly, driving significant advancements in digitalization that have transformed day to day lives. Its growing influence on consumers and the economy has increased the risk of cyberattacks. Cybercriminals exploited network misconfigurations and security vulnerabilities during these transitions. Among countless cyberattacks, phishing remains the most common form of cybercrime. Phishing via malicious Uniform Resource Locator (URL)s threatens potential victims by posing as an imposter and stealing critical and sensitive data. An increase in cyberattacks using phishing needs immediate attention to find a scalable solution. Earlier techniques like blacklisting, signature matching, and regular expression method are insufficient because of the requirement to keep updating the rule engine or signature database regularly. Significant research has recently been conducted on using Machine Learning (ML) models to detect malicious URLs. In this study, the authors have provided a study highlighting the importance of significant feature selection for training ML models for detecting malicious URLs. Pearson correlation is employed in this study for selecting significant features, and the outcome demonstrates that in terms of accuracy and other performance indices, the Random Forest classifier outperforms the other classifiers.

@article{Sangra2024,
  title     = {Malicious Website Detection Using Random Forest and Pearson Correlation for Effective Feature Selection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Esha Sangra and Renuka Agrawal and Pravin Ramesh Gundalwar and Kanhaiya Sharma and Divyansh Bangri and Debadrita Nandi},
  doi       = {10.14569/IJACSA.2024.0150876},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150876}
}