TY  - JOUR
T1  - VNF Anomaly Detection Method based on Unsupervised Machine Learning
AU  - Heo, Seondong 
AU  - Jeong, Seunghoon 
AU  - Yun, Hosang 
JO  - Journal of KIISE, JOK
PY  - 2022
DA  - 2022/1/14
DO  - 10.5626/JOK.2022.49.9.780
KW  - software-defined networking
KW  - network function virtualization
KW  - virtualized network function
KW  - machine learning
KW  - anomaly detection
AB  - 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.