Volume 10, Number 3

A Comparative Analysis of Different Feature Set on the Performance of Different Algorithms in
Phishing Website Detection

  Authors

Hajara Musa¹, Bala Modi¹, Ismail Abdulkarim Adamu², Ali Ahmad Aminu¹, Hussaini Adamu¹ and Yahaya Ajiya¹, ¹Gombe State University, Nigeria and ²Gombe State Polytechnic, Nigeria

  Abstract

Reducing the risk pose by phishers and other cybercriminals in the cyber space requires a robust and automatic means of detecting phishing websites, since the culprits are constantly coming up with new techniques of achieving their goals almost on daily basis. Phishers are constantly evolving the methods they used for luring user to revealing their sensitive information. Many methods have been proposed in past for phishing detection. But the quest for better solution is still on. This research covers the development of phishing website model based on different algorithms with different set of features in order to investigate the most significant features in the dataset

  Keywords

Machine learning, Feature selection, Phishing, XGBoost, Random Forest (RF) and Probabilistic Neural Network (PNN)