The Anomaly Detection Mechanism Using Extreme Learning Machine for Service Function Chaining

Abstract

The age of the wireless network already advances to the fifth generation (5G) era. With software-defined networking (SDN) and network function virtualization (NFV), various scenarios can be implemented in the 5G network. Cloud computing, for example, is one of the important application scenarios for implementing SDN/NFV solutions. The emerging container technologies, such as Docker, can provide more agile service provisioning than virtual machines can do in cloud environments. It is a trend that virtual network functions (VNFs) tend to be deployed in the form of containers. The services provided by clouds can be formed by service function chaining (SFC) consisting of containerized VNFs. Nevertheless, the challenges and limitation regarding SFCs are reported in the literature. Various network services are bound to rely heavily on these novel technologies, however, the development of related technologies often emphasizes functions and ignores security issues. One noticeable issue is the SFC integrity. In brief, SFC integrity concerns whether the paths that traffic flows really pass by and the ones of service chains that are predefined are consistent. In order to examine SFC integrity in the cloud-native environment of 5G network, we propose a framework that can be integrated with NFV management and orchestration (MANO) in this work. The core of this framework is the anomaly detection mechanism for SFC integrity. The learning algorithm of our mechanism is based on extreme learning machine (ELM). The proposed mechanism is evaluated by its performance such as the accuracy of our ELM model. This paper concludes with discussions and future research work.