Machine Learning-Based Phishing Attack Detection

This paper explores machine learning techniques and evaluates their performances when trained to perform against datasets consisting of features that can differentiate between a Phishing Website and a safe one. This capability of telling these sites apart from one another is vital in the modern-day internet surfing. As more and more of our resources shift online, one vulnerability and a leak of sensitive information by someone could bring everything down in a connected network. This paper's objective through this research is to highlight the best technique for identifying one of the most commonly occurring cyberattacks and thus allow faster identification and blacklisting of such sites, therefore leading to a safer and more secure web surfing experience for everyone. To achieve this, we describe each of the techniques we look into in great detail and use different evaluation techniques to portray their performance visually. After pitting all of these techniques against each other, we have concluded with an explanation in this paper that Random Forest Classifier does indeed work best for Phishing Website Detection.

@article{Hossain2020,
  title     = {Machine Learning-Based Phishing Attack Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {9},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Sohrab Hossain and Dhiman Sarma and Rana Joyti Chakma},
  doi       = {10.14569/IJACSA.2020.0110945},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110945}
}