  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.
