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.