Method for Network-Anomaly Detection and Failure-Scale Estimation

Abstract

In this study, we propose a novel method for network-anomaly detection and failure-scale estimation using autoencoders, which are a type of neural network. The proposed method first divides the network into several groups. Subsequently, anomalies are detected using an autoencoder for each intergroup traffic, and the failure-scale is estimated from the number of autoencoders that have detected anomalies. We experimentally investigated anomaly detection during communication through a virtual network built using the network emulator Mininet and confirmed that the proposed method can successfully detect anomalies and estimate the failure scale.