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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.

Important components of the electric energy distribution systems are primary and secondary substations. Due to the incorporation of legacy communication infrastructure in these systems, they often have inherent cyber-security vulnerabilities. Further, traditional intrusion defence strategies for IT systems are often not applicable. In order to improve cyber-security in substation networks, this paper presents a neural net-based monitoring system. Further, to evaluate the applicability of the system, all experiments were conducted on a real test bed, which represents the substation domain as close as possible to reality. The proposed monitoring system covers several tasks. First, relevant network packets are acquired from network traffic and analysed. Based on these packets statistical features are extracted. Then, classes are defined, and a normal behaviour model of the network is trained by the neural net. New network traffic is compared to the model, in order to determine the nature of the traffic and identify potential anomalies. Finally, the monitoring system is evaluated by conducting several supervised and unsupervised network attacks against the test bed.

In this paper we present a method to detect Man-in-the-Middle attacks on industrial control systems. The approach uses anomaly detection by developing a model of normal behaviour of the industrial control system network. To come as close as possible to reality a simple industrial system, a conveyor belt with sensors and actuators, was set up with controllers widely used in industry. A machine learning approach based on the k-Nearest Neighbors algorithm with Bregman divergence was used to define a model of normal (valid) behaviour. Afterwards Man-in-the-Middle attacks were launched against the system and its behaviour during the attack was compared to the valid behaviour model. The results show that the approach taken was able to detect such attacks with satisfactory accuracy.