Network Anomaly Detection Based on In-band Network Telemetry with RNN

Network anomaly detection is a technology that detects malicious attacks occurring in the network in real time. This paper proposes an anomaly detection algorithm using machine learning with In-band network telemetry (INT) as input features. INT is a network monitoring method that offers detailed network information in real time. To test the proposed anomaly detection method using INT, the experiment is conducted. We created normal and abnormal flows in a virtual network environment, collected INT data, and conducted an anomaly detection experiment using this data. The experimental results of the proposed method show that INT based anomaly detection can achieve better performance.