Service Function Chain Anomaly Detection Based on Distributed Generative Adversarial Network in Network Slicing Scenario

TANG Lun; WANG Kai; ZHANG Yue; ZHOU Xinlong; CHEN Qianbin

doi:10.11999/JEIT211261

Volume 45 Issue 1

Jan. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(1): 262-271

TANG Lun, WANG Kai, ZHANG Yue, ZHOU Xinlong, CHEN Qianbin. Service Function Chain Anomaly Detection Based on Distributed Generative Adversarial Network in Network Slicing Scenario[J]. Journal of Electronics & Information Technology, 2023, 45(1): 262-271. doi: 10.11999/JEIT211261

Citation:

TANG Lun, WANG Kai, ZHANG Yue, ZHOU Xinlong, CHEN Qianbin. Service Function Chain Anomaly Detection Based on Distributed Generative Adversarial Network in Network Slicing Scenario[J]. Journal of Electronics & Information Technology, 2023, 45(1): 262-271. doi: 10.11999/JEIT211261

Citation:

TANG Lun, WANG Kai, ZHANG Yue, ZHOU Xinlong, CHEN Qianbin. Service Function Chain Anomaly Detection Based on Distributed Generative Adversarial Network in Network Slicing Scenario[J]. Journal of Electronics & Information Technology, 2023, 45(1): 262-271. doi: 10.11999/JEIT211261

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Service Function Chain Anomaly Detection Based on Distributed Generative Adversarial Network in Network Slicing Scenario

doi: 10.11999/JEIT211261

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Key Laboratory of Mobile Communications, Chongqing University of Post and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (62071078), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-M201800601), Sichuan and Chongqing Key R&D Projects (2021YFQ0053)

Received Date: 2021-11-12
Rev Recd Date: 2022-04-02

Available Online: 2022-04-17

Publish Date: 2023-01-17

Abstract

Abstract

For the problem of Service Function Chain (SFC) anomalies due to hardware and software anomalies in network slicing scenarios, a Distributed Generative Adversarial Network (GAN)-based Time Series anomaly detection model (DTSGAN) is proposed. First, to learn the characteristics of normal data in SFC, a distributed GAN architecture is proposed for anomaly detection of multiple Virtual Network Functions (VNFs) contained in SFC. Then, a feature extractor based on sliding window data is constructed for time series data, and the feature sequence is obtained by extracting two derived characteristics and eight statistical features of the data to mine the deep-level features. Finally, in order to learn and reconstruct data characteristics, a three-layer codec constructed by Time Convolutional Network (TCN) and Auto-Encoder (AE) is proposed as a distributed generator, which measures the difference between reconstructed data and input data by anomaly score function to detect the state of VNF, and then completes the anomaly detection of SFC. The effectiveness and stability of the proposed model are verified on the dataset Clearwater using four evaluation metrics: accuracy, precision, recall and F1 score.
- Anomaly detection,
- Service Function Chain(SFC),
- Generative Adversarial Networks(GAN)

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References(12)

References

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