Multi-Class Intrusion Detection Based on Transformer for IoT Networks Using CIC-IoT-2023 Dataset

被引:3
作者
Tseng, Shu-Ming [1 ]
Wang, Yan-Qi [1 ]
Wang, Yung-Chung [2 ]
机构
[1] Natl Taipei Univ Technol, Dept Elect Engn, Taipei 10608, Taiwan
[2] Natl Taipei Univ Technol, Dept Elect Engn, Taipei 10608, Taiwan
关键词
Internet of Things; intrusion detection; deep learning; CIC-IoT-202; transformer;
D O I
10.3390/fi16080284
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This study uses deep learning methods to explore the Internet of Things (IoT) network intrusion detection method based on the CIC-IoT-2023 dataset. This dataset contains extensive data on real-life IoT environments. Based on this, this study proposes an effective intrusion detection method. Apply seven deep learning models, including Transformer, to analyze network traffic characteristics and identify abnormal behavior and potential intrusions through binary and multivariate classifications. Compared with other papers, we not only use a Transformer model, but we also consider the model's performance in the multi-class classification. Although the accuracy of the Transformer model used in the binary classification is lower than that of DNN and CNN + LSTM hybrid models, it achieves better results in the multi-class classification. The accuracy of binary classification of our model is 0.74% higher than that of papers that also use Transformer on TON-IOT. In the multi-class classification, our best-performing model combination is Transformer, which reaches 99.40% accuracy. Its accuracy is 3.8%, 0.65%, and 0.29% higher than the 95.60%, 98.75%, and 99.11% figures recorded in papers using the same dataset, respectively.
引用
收藏
页数:25
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