An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment
With the advent of the digital information age, new data services such as virtual reality, industrial Internet, and cloud computing have proliferated in recent years. As a result, it increases operator demand for 5G bearer networks by providing features such as high transmission capacity, ultra-long...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/19/7436 |
| _version_ | 1827653017346768896 |
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| author | Lifeng Lei Liang Kou Xianghao Zhan Jilin Zhang Yongjian Ren |
| author_facet | Lifeng Lei Liang Kou Xianghao Zhan Jilin Zhang Yongjian Ren |
| author_sort | Lifeng Lei |
| collection | DOAJ |
| description | With the advent of the digital information age, new data services such as virtual reality, industrial Internet, and cloud computing have proliferated in recent years. As a result, it increases operator demand for 5G bearer networks by providing features such as high transmission capacity, ultra-long transmission distance, network slicing, and intelligent management and control. Software-defined networking, as a new network architecture, intends to increase network flexibility and agility and can better satisfy the demands of 5G networks for network slicing. Nevertheless, software-defined networking still faces the challenge of network intrusion. We propose an abnormal traffic detection method based on the stacking method and self-attention mechanism, which makes up for the shortcoming of the inability to track long-term dependencies between data samples in ensemble learning. Our method utilizes a self-attention mechanism and a convolutional network to automatically learn long-term associations between traffic samples and provide them to downstream tasks in sample embedding. In addition, we design a novel stacking ensemble method, which computes the sample embedding and the predicted values of the heterogeneous base learner through the fusion module to obtain the final outlier results. This paper conducts experiments on abnormal traffic datasets in the software-defined network environment, calculates precision, recall and F1-score, and compares and analyzes them with other algorithms. The experimental results show that the method designed in this paper achieves 0.9972, 0.9996, and 0.9984 in multiple indicators of precision, recall, and F1-score, respectively, which are better than the comparison methods. |
| first_indexed | 2024-03-09T21:10:11Z |
| format | Article |
| id | doaj.art-7b44807f59af4f5a92c2691bdf5cf5c3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:10:11Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7b44807f59af4f5a92c2691bdf5cf5c32023-11-23T21:49:10ZengMDPI AGSensors1424-82202022-09-012219743610.3390/s22197436An Anomaly Detection Algorithm Based on Ensemble Learning for 5G EnvironmentLifeng Lei0Liang Kou1Xianghao Zhan2Jilin Zhang3Yongjian Ren4Computer & Software School, Hangzhou Dianzi University, Hangzhou 310018, ChinaComputer & Software School, Hangzhou Dianzi University, Hangzhou 310018, ChinaComputer & Software School, Hangzhou Dianzi University, Hangzhou 310018, ChinaComputer & Software School, Hangzhou Dianzi University, Hangzhou 310018, ChinaComputer & Software School, Hangzhou Dianzi University, Hangzhou 310018, ChinaWith the advent of the digital information age, new data services such as virtual reality, industrial Internet, and cloud computing have proliferated in recent years. As a result, it increases operator demand for 5G bearer networks by providing features such as high transmission capacity, ultra-long transmission distance, network slicing, and intelligent management and control. Software-defined networking, as a new network architecture, intends to increase network flexibility and agility and can better satisfy the demands of 5G networks for network slicing. Nevertheless, software-defined networking still faces the challenge of network intrusion. We propose an abnormal traffic detection method based on the stacking method and self-attention mechanism, which makes up for the shortcoming of the inability to track long-term dependencies between data samples in ensemble learning. Our method utilizes a self-attention mechanism and a convolutional network to automatically learn long-term associations between traffic samples and provide them to downstream tasks in sample embedding. In addition, we design a novel stacking ensemble method, which computes the sample embedding and the predicted values of the heterogeneous base learner through the fusion module to obtain the final outlier results. This paper conducts experiments on abnormal traffic datasets in the software-defined network environment, calculates precision, recall and F1-score, and compares and analyzes them with other algorithms. The experimental results show that the method designed in this paper achieves 0.9972, 0.9996, and 0.9984 in multiple indicators of precision, recall, and F1-score, respectively, which are better than the comparison methods.https://www.mdpi.com/1424-8220/22/19/7436self-attentionensemble learninganomaly detectionSDN5G |
| spellingShingle | Lifeng Lei Liang Kou Xianghao Zhan Jilin Zhang Yongjian Ren An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment Sensors self-attention ensemble learning anomaly detection SDN 5G |
| title | An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment |
| title_full | An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment |
| title_fullStr | An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment |
| title_full_unstemmed | An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment |
| title_short | An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment |
| title_sort | anomaly detection algorithm based on ensemble learning for 5g environment |
| topic | self-attention ensemble learning anomaly detection SDN 5G |
| url | https://www.mdpi.com/1424-8220/22/19/7436 |
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