计算机科学
异常检测
人工智能
深度学习
机器学习
虚拟网络
人工神经网络
网络功能虚拟化
功能(生物学)
虚拟化
服务(商务)
Boosting(机器学习)
分布式计算
操作系统
云计算
经济
经济
生物
进化生物学
作者
Chungjun Lee,Hong Jiang,DongNyeong Heo,Heeyoul Choi
标识
DOI:10.1109/ictc52510.2021.9621043
摘要
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.
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