An Anomaly Detection Architecture in SDN using computational intelligence
SDN, Anomalie detection, Machine learning
Emerging technologies such as the Cloud, 5G, Internet of Things (IoT) and edge computing encompass controlling and networking millions of devices every day. Managing traditional networks with millions of devices is a complex task as it requires configuring data traffic routes on each device in the network. With it centralized network controller, Software-Defined Networking (SDN) can help simplify the configuration and management of a network with this many devices. Many studies have researched the use of different computational intelligence (CI) methods to detect anomalies in SDN, this work defines a framework to validate, promote and explain, using Explainable AI, any of these CI methods that best detects each of the different types of anomalies, and also define architecture based on hexagonal microservices and with a unique data model based on the application and information framework, Open Digital Architecture, from the TM Forum