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

唐伦; 王恺; 张月; 周鑫隆; 陈前斌

doi:10.11999/JEIT211261

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

doi: 10.11999/JEIT211261

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
重庆邮电大学移动通信重点实验室重庆 400065

基金项目: 国家自然科学基金(62071078)，重庆市教委科学技术研究项目(KJZD-M201800601)，川渝联合实施重点研发项目(2021YFQ0053)

详细信息

作者简介:
唐伦：男，教授，博士生导师，研究方向为新一代无线通信网络、异构蜂窝网络、软件定义无线网络等

王恺：女，硕士生，研究方向为虚拟化网络切片、异常检测等

张月：女，硕士生，研究方向为5G网络故障检测、机器学习等

周鑫隆：男，硕士生，研究方向为虚拟化网络切片、流量预测等

陈前斌：男，教授，博士生导师，研究方向为个人通信、多媒体信息处理与传输、下一代移动通信网络、异构蜂窝网络等

通讯作者:
王恺　845787457@qq.com

中图分类号: TN929.5
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- 被引次数: 7
出版历程
- 收稿日期: 2021-11-12
- 修回日期: 2022-04-02
- 网络出版日期: 2022-04-17
- 刊出日期: 2023-01-17

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

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)

摘要

摘要: 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。
- 异常检测 /
- 服务功能链 /
- 生成对抗网络
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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图 1 网络切片管理场景

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图 2 DTSGAN模型架构

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图 3 DTSGAN模型整体流程

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图 4 特征提取步骤

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图 5 损失函数

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图 6 滑动窗口尺寸对性能指标的影响

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图 7 4种方案性能指标对比

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图 8 算法稳定性对比

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算法1　DTSGAN在MANO中的训练
输入：数据流 $({{\boldsymbol{F}}_i},{{\boldsymbol{z}}_i}),({{{ {\boldsymbol {\boldsymbol{F}}}}}_i},{{{\hat {\boldsymbol z}}}_i})$
输出：误差项 ${\boldsymbol{B}}_i^1,{\boldsymbol{B}}_i^2,{\boldsymbol{B}}_i^3$ ，鉴别器输出 $D({{{\hat {\boldsymbol F}}}_i},{{{\hat {\boldsymbol z}}}_i})$
(1) function ${{\rm{MANODIS}}} (({ {\boldsymbol{F} }_i},{ {\boldsymbol{z} }_i}),({ { {\hat {\boldsymbol F} } }_i},{ { {\hat {\boldsymbol z} } }_i}),d,k)$ // 鉴别器 $D$ 训　　练函数
(2) 　for $j \in [1,b]$ do // 鉴别器 $D$ 训练轮数为 $b$
(3) 　　for $i \in [1,k]$ do // ${\text{E}}{{\text{M}}_i}$ 的个数为 $k$
(4) 　　　从 ${\text{E}}{{\text{M}}_i}$ 中获得数据流 $({{\boldsymbol{F}}_i},{{\boldsymbol{z}}_i}),({{{\hat {\boldsymbol F}}}_i},{{{\hat {\boldsymbol z}}}_i})$
(5) 　　　计算第 $i$ 个梯度 $\Delta {{\boldsymbol{w}}_{{\text{d}}i}}$
(6) 　　　if $j = d$ do
(7) 　　　　分别根据式(8)—式(10)计算 $G_i^1$ , $G_i^2$ , $G_i^3$ 的误差项 ${\boldsymbol{B}}_i^1$ , 　　　　　　 ${\boldsymbol{B}}_i^2$ , ${\boldsymbol{B}}_i^3$
(8) 　　　　将鉴别器结果 ${\boldsymbol{B}}_i^1,{\boldsymbol{B}}_i^2,{\boldsymbol{B}}_i^3,D({{{\hat {\boldsymbol F}}}_i},{{{\hat {\boldsymbol z}}}_i})$ 发送至相应的　　　　　　 ${\text{E}}{{\text{M}}_i}$
(9) 　　　end if
(10) 　　end for
(11) 　　对 $k$ 个梯度求平均值得到 $\Delta {{\boldsymbol{w}}_{\text{d}}}$
(12) 　　使用 $\Delta {{\boldsymbol{w}}_{\text{d}}}$ 更新鉴别器 $D$ 参数 ${{\boldsymbol{w}}_{\text{d}}}$
(13) 　end for
(14) end function

下载: 导出CSV

算法2　DTSGAN在 ${\text{E}}{{\text{M}}_i}$ 中的训练
输入：特征序列 ${{\mathbf{F}}_i}$ ，鉴别器反馈 ${\mathbf{B}}_i^1,{\mathbf{B}}_i^2,{\mathbf{B}}_i^3$
输出：数据流 $({{\mathbf{F}}_i},{{\mathbf{z}}_i}),({{{\hat {\boldsymbol F}}}_i},{{{\hat {\boldsymbol z}}}_i})$
(1) function ${\text{EMGEN}}({{\mathbf{F}}_i},{\mathbf{B}}_i^1,{\mathbf{B}}_i^2,{\mathbf{B}}_i^3,L)$ // 生成器 ${G_i}$ 训练函数
(2) 　编码器 $G_i^1$ 提取潜在表示 ${{\mathbf{z}}_i}$
(3) 　解码器 $G_i^2$ 重构特征序列 ${{{\hat {\boldsymbol F}}}_i}$
(4) 　编码器 $G_i^3$ 重构潜在表示 ${{{\hat {\boldsymbol z}}}_i}$
(5) 　将数据流 $({{\mathbf{F}}_i},{{\mathbf{z}}_i}),({{{\hat {\boldsymbol F}}}_i},{{{\hat {\boldsymbol z}}}_i})$ 发送至鉴别器 $D$
(6) 　if $L \ne 1$ do // 训练轮数为 $L$
(7) 　　从鉴别器 $D$ 获取误差项 ${\mathbf{B}}_i^1,{\mathbf{B}}_i^2,{\mathbf{B}}_i^3$
(8) 　　分别根据式(15)—式(17)计算计算 $G_i^1$ , $G_i^2$ , $G_i^3$ 的梯度　　　　 $\Delta {\mathbf{w}}_i^{1,l}$ , $\Delta {\mathbf{w}}_i^{2,l}$ , $\Delta {\mathbf{w}}_i^{3,l}$
(9) 　　使用 $\Delta {\mathbf{w}}_i^{1,l}$ , $\Delta {\mathbf{w}}_i^{2,l}$ , $\Delta {\mathbf{w}}_i^{3,l}$ 更新生成器 ${G_i}$ 参数　　　　 ${\mathbf{w}}_i^1,{\mathbf{w}}_i^2,{\mathbf{w}}_i^3$
(10) 　end if
(11) end function

下载: 导出CSV

表 1 滑动窗口尺寸设置

	1	2	3	4	5	6	7
衍生特性滑动窗口尺寸 $s$	2	6	8	8	10	14	14
统计特征滑动窗口尺寸 $f$	2	2	4	6	8	10	12

下载: 导出CSV

参考文献(12)

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