TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Tri-training algorithm based on cross entropy and K-nearest neighbors for network intrusion detection |
| 영문제목(English Title) |
Tri-training algorithm based on cross entropy and K-nearest neighbors for network intrusion detection |
| 저자(Author) |
Jia Zhao
Song Li
Runxiu Wu
Yiying Zhang
Bo Zhang
Longzhe Han
|
| 원문수록처(Citation) |
VOL 16 NO. 12 PP. 3889 ~ 3903 (2022. 12) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
To address the problem of low detection accuracy due to training noise caused by mislabeling when Tri-training for network intrusion detection (NID), we propose a Tri-training algorithm based on cross entropy and K-nearest neighbors (TCK) for network intrusion detection. The proposed algorithm uses cross-entropy to replace the classification error rate to better identify the difference between the practical and predicted distributions of the model and reduce the prediction bias of mislabeled data to unlabeled data; K-nearest neighbors are used to remove the mislabeled data and reduce the number of mislabeled data. In order to verify the effectiveness of the algorithm proposed in this paper, experiments were conducted on 12 UCI datasets and NSL-KDD network intrusion datasets, and four indexes including accuracy, recall, F-measure and precision were used for comparison. The experimental results revealed that the TCK has superior performance than the conventional Tri-training algorithms and the Tri-training algorithms using only cross-entropy or K-nearest neighbor strategy. |
| 키워드(Keyword) |
network intrusion detection (NID)
semi-supervised learning
Tri-training
cross entropy
K-nearest neighbors
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| 파일첨부 |
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