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VNF Anomaly Detection Method based on Unsupervised Machine Learning
Seondong Heo, Seunghoon Jeong, Hosang Yun
http://doi.org/10.5626/JOK.2022.49.9.780
By applying virtualization technology to telecommunication networks, it is possible to reduce hardware dependencies and provide flexible control and management to the operators. In addition, since Capital Expenditure (CAPEX) and Operating Expenditure (OPEX) can be reduced by utilizing the technology, modern telco operators and service providers are using Software-Defined Networking(SDN) and Network Function Virtualization (NFV) technology to provide services more efficiently. As SDN and NFV are widely used, cyber attacks on Vitualized Network Functions (VNF) that degrade the quality of service or cause service denial are increasing. In this study, we propose a VNF anomaly detection method based on unsupervised machine learning techniques that models the steady states of VNFs and detects abnormal states caused by cyber attacks.
Learning-based QoS Path Prediction Method in SDN Environment
Seunghoon Jeong, Seondong Heo, Hosang Yun
http://doi.org/10.5626/JOK.2021.48.11.1241
When Quality of Service (QoS) is supported by flow path control in Software-Defined Networking (SDN) environment, the current simple least cost path finding method can cause inefficient rerouting problems. The measured performance of the flow path derived based on the link quality may differ from the predicted performance. In particular, in the case of sequential QoS condition search for candidate paths, the effectiveness of path-based QoS support may decrease due to repeatedly searching for the same path previously identified as the final path. In this paper, we propose a learning-based QoS path search model. The model learns the path that finally satisfies the QoS conditions according to the network state, and predicts the QoS path for the network state when rerouting is required. The experiment shows that this learning model can reduce unnecessary path iteration search costs given the similar network conditions, and is more effective than other learning-based models in a service environment that requires rapid QoS quality restoration.
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