网络切片场景下基于分布式生成对抗网络的服务功能链异常检测

针对网络切片场景中，由于软硬件异常而导致服务功能链(SFC)异常的问题，该文提出一种基于分布式生成对抗网络(GAN)的时间序列异常检测模型(DTSGAN)。首先，为学习SFC中正常数据的特征，提出分布式GAN架构，对SFC中包含的多个虚拟网络功能(VNF)进行异常检测；其次，针对时间序列数据构建一种基于滑动窗口数据特征提取器，通过提取数据的两种衍生特性和8种统计特征以挖掘深层次特征，得到特征序列；最后，为学习并重构数据特征，提出时间卷积网络(TCN)与自动编码器(AE)构建的3层编解码器作为分布式生成器，生成器通过异常得分函数衡量重构数据与输入数据的差异以检测VNF的状态，进而完成SFC的异常检测。在数据集Clearwater上采用准确率、精确率、召回率和F1分数这4个性能指标验证了该文所提模型的有效性和稳定性。

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

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.