Unsupervised Learning based Intrusion Detection for GOOSE Messages in Digital Substation

Abstract

Implementation of IEC-61850 in the electrical substations has transformed them into digital substations. However, this has also exposed the communication network of the substation to cyberattacks, where an attacker can temper with GOOSE messages. To protect digital substations from potential cyberattacks, an effective intrusion detection system is very much required. Hence, in this work an unsupervised learning based intrusion detection system is proposed, which can detect the anomalies in GOOSE packets transmitted within the substation. Two unsupervised learning techniques, DBSCAN and autoencoder, are used in this work to develop an intrusion detection system, and their performance in detecting payload corruption is evaluated through numerical simulations.