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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.
A Markov Approximation-Based Approach for Network Service Chain Embedding
Pham Chuan, Minh N. H. Nguyen, Choong Seon Hong
http://doi.org/10.5626/JOK.2017.44.7.719
To reduce management costs and improve performance, the European Telecommunication Standards Institute (ETSI) introduced the concept of network function virtualization (NFV), which can implement network functions (NFs) on cloud/datacenters. Within the NFV architecture, NFs can share physical resources by hosting NFs on physical nodes (commodity servers). For network service providers who support NFV architectures, an efficient resource allocation method finds utility in being able to reduce operating expenses (OPEX) and capital expenses (CAPEX). Thus, in this paper, we analyzed the network service chain embedding problem via an optimization formulation and found a close-optimal solution based on the Markov approximation framework. Our simulation results show that our approach could increases on average CPU utilization by up to 73% and link utilization up to 53%.
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