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Software-defined networking (SDN) and network function virtualization (NFV)have enabled the efficient provision of network service. However, they alsoraised new tasks to monitor and ensure the status of virtualized service, andanomaly detection is one of such tasks. There have been many data-drivenapproaches to implement anomaly detection system (ADS) for virtual networkfunctions in service function chains (SFCs). In this paper, we aim to developmore advanced deep learning models for ADS. Previous approaches used learningalgorithms such as random forest (RF), gradient boosting machine (GBM), or deepneural networks (DNNs). However, these models have not utilized sequentialdependencies in the data. Furthermore, they are limited as they can only applyto the SFC setting from which they were trained. Therefore, we propose severalsequential deep learning models to learn time-series patterns and sequentialpatterns of the virtual network functions (VNFs) in the chain with variablelengths. As a result, the suggested models improve detection performance andapply to SFCs with varying numbers of VNFs.

Sequential Deep Learning Architectures for Anomaly Detection in Virtual Network Function Chains