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Abstract
The advent of the Smart Grid (SG) raises severe cybersecurity risks that can lead to devastating consequences. In this paper, we present a novel anomaly-based Intrusion Detection System (IDS), called ARIES (smArt gRid Intrusion dEtection System), which is capable of protecting efficiently SG communications. ARIES combines three detection layers that are devoted to recognising possible cyberattacks and anomalies against (a) network flows, (b) Modbus/Transmission Control Protocol (TCP) packets and (c) operational data. Each detection layer relies on a Machine Learning (ML) model trained using data originating from a power plant. In particular, the first layer (network flow-based detection) performs a supervised multiclass classification, recognising Denial of Service (DoS), brute force attacks, port scanning attacks and bots. The second layer (packet-based detection) detects possible anomalies related to the Modbus packets, while the third layer (operational data based detection) monitors and identifies anomalies upon operational data (i.e., time series electricity measurements). By emphasising on the third layer, the ARIES Generative Adversarial Network (ARIES GAN) with novel error minimisation functions was developed, considering mainly the reconstruction difference. Moreover, a novel reformed conditional input was suggested, consisting of random noise and the signal features at any given time instance. Based on the evaluation analysis, the proposed GAN network overcomes the efficacy of conventional ML methods in terms of Accuracy and the F1 score.
Highlights
The Smart Grid (SG) as the convergence of the electrical system engineering with the Information and Communication Technology (ICT) is expected to eliminate the significant limitations and shortcomings of the existing electrical grid, such as the energy conversation, the demand response and the optimal utilisation of the various assets
It should be noted that ARIES is a Network Intrusion Detection System (IDS) (NIDS) capable of receiving the network traffic data generated in a subnet (i.e., ARIES is installed in a centralised server, which aggregates the network traffic generated by all devices utilising a Switched Port Analyser (SPAN) port)
It is noteworthy that regarding the first detection layer of ARIES, the following metrics were calculated by measuring entirely True Positives (TP), True Negatives (TN), FP and False Negatives (FN) for each class
Summary
The Smart Grid (SG) as the convergence of the electrical system engineering with the Information and Communication Technology (ICT) is expected to eliminate the significant limitations and shortcomings of the existing electrical grid, such as the energy conversation, the demand response and the optimal utilisation of the various assets. The autonomous nature of the IoT devices to communicate with each other without human intervention increases significantly the cybersecurity concerns Both academia and industry have identified novel authentication and access control mechanisms that can enhance the overall security and safety of SG [6,7,8]. The IEC 62351 standard [9] defines appropriate solutions aiming to meet the security gaps of the vulnerable SCADA communication protocols These solutions may be efficient, their implementation and validation in real conditions is a challenging step since the procedures of the electrical grid should operate continuously. Concerning the operational data-based detection (third detection layer), a novel Generative Adversarial Network (GAN) [10,11] called ARIES GAN was implemented, which overcomes the detection performance of conventional ML methods.
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