Anomaly detection in substation networks

Fundamental components of the distribution systems of electric energy are primary and secondary substation networks. Considering the incorporation of legacy communication infrastructure in these systems, they often have in- herent cybersecurity vulnerabilities. Moreover, traditional intrusion defence strategies for IT systems are often not applicable. With the aim to improve cybersecurity in substation networks, in this paper we present two methods for monitoring SCADA system: the first one exploiting neural networks, while the second one is based on formal methods. To evaluate the effective- ness of the proposed methods, we conducted experiments on a real test bed representing the substation domain as close to real-world as possible. From this test bed we collect data during normal operation and during situations where the system is under attack. To this end several different types of attack are conducted. The data collected is used to test two versions of the mon- itoring system: one based on machine learning with a neural network and one using a model-checking approach. Moreover, the two proposed models are tested with new data to evaluate their performance. The experiments demonstrate that both methods obtain an accuracy greater than 90%. In particular, the methodology based on formal methods achieves better per- formance if compared to the one based on neural networks.