| Title | A Phishing Detection Method Based on Data Mining |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Li, Chunzhi |
| Conference Name | 2021 3rd International Conference on Applied Machine Learning (ICAML) |
| Keywords | data mining, Deep Learning, feature extraction, Forestry, Human Behavior, Learning systems, lexical features, Malicious URL, Network security, Neural networks, phishing, pubcrawl, Random Forest |
| Abstract | Data mining technology is a very important technology in the current era of data explosion. With the informationization of society and the transparency and openness of information, network security issues have become the focus of concern of people all over the world. This paper wants to compare the accuracy of multiple machine learning methods and two deep learning frameworks when using lexical features to detect and classify malicious URLs. As a result, this paper shows that the Random Forest, which is an ensemble learning method for classification, is superior to 8 other machine learning methods in this paper. Furthermore, the Random Forest is even superior to some popular deep neural network models produced by famous frameworks such as TensorFlow and PyTorch when using lexical features to detect and classify malicious URLs. |
| DOI | 10.1109/ICAML54311.2021.00050 |
| Citation Key | li_phishing_2021 |
A Phishing Detection Method Based on Data Mining