Software-defined networking (SDN) and network function virtualization (NFV) have enabled the efficient provision of network service. However, they also raised new tasks to monitor and ensure the status of virtualized service, and anomaly detection is one of such tasks. There have been many data-driven approaches to implement anomaly detection system (ADS) for virtual network functions in service function chains (SFCs). In this paper, we aim to develop more advanced deep learning models for ADS. Previous approaches used learning algorithms such as random forest (RF), gradient boosting machine (GBM), or deep neural networks (DNNs). However, these models have not utilized sequential dependencies in the data. Furthermore, they are limited as they can only apply to the SFC setting from which they were trained. Therefore, we propose several sequential deep learning models to learn time-series patterns and sequential patterns of the virtual network functions (VNFs) in the chain with variable lengths. As a result, the suggested models improve detection performance and apply to SFCs with varying numbers of VNFs.
翻译:软件定义网络(SDN)和网络功能虚拟化(NFV)使得能够有效地提供网络服务,然而,它们也提出了监测和确保虚拟化服务状况的新任务,发现异常现象也是其中一项任务。在服务功能链(SFCs)中,对虚拟网络功能实施异常现象探测系统(ADS)有许多数据驱动的方法。在本文件中,我们的目标是为ADS开发更先进的深层次学习模式。以前的方法使用了随机森林、梯度加速机(GBM)或深神经网络(DNNS)等学习算法。但是,这些模型没有利用数据中的相继依赖性。此外,这些模型是有限的,因为它们只能适用于他们接受培训的SFC环境。因此,我们提出若干顺序深层次学习模型,以学习时间序列模式和不同长度的虚拟网络功能(VNFS)在链中的顺序模式。结果,建议的模式改进了探测性能,并适用于数量不同的SFCs。