Academic Journal of Computing & Information Science, 2023, 6(5); doi: 10.25236/AJCIS.2023.060512.
Chengge Duan1, Minze Wang2, Xin Lu2, Junming Wang3
1Suzhou Public Security Bureau, Suzhou, Jiangsu, China
2School of Social Computing, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, China
3The Third Research Institute of Ministry of Public Security, Shanghai, China
In today's Internet age, phishing attacks are a common means of cyberattacks. Most existing URL-based anti-phishing technologies are simple and effective, but lagging, while machine learning and deep learning-based approaches can effectively improve detection efficiency. This study advocates the use of TF-IDF for website data preprocessing followed by a random forest model to achieve phishing website feature classification. The final experimental results show that the model accuracy of the random forest algorithm based on machine learning to judge phishing websites is high and the anti-phishing capability is superior.
Phishing Website Detection; Random Forest Model; TF-IDF; Machine Learning
Chengge Duan, Minze Wang, Xin Lu, Junming Wang. A phishing website detection system based on machine learning methods. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 5: 91-94. https://doi.org/10.25236/AJCIS.2023.060512.
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