A Fixed-Latency Architecture to Secure GOOSE and Sampled Value Messages in Substation Systems

International Electrotechnical Commission (IEC) 62351-6 standard specifies the security mechanisms to protect real-time communications based on IEC 61850. Generic Object Oriented Substation Events (GOOSE) and Sampled Value (SV) messages must be generated, transmitted and processed in less than 3 ms, which challenges the introduction of IEC 62351-6. After evaluating the security threats to IEC 61850 communications and the state of the art in GOOSE and SV security, this work presents a novel architecture based on wire-speed processing able to provide message authentication and confidentiality. This architecture has been implemented and tested to evaluate its performance, resource usage, and the latency introduced. Other proposals in the scientific literature do not support real-time traffic, so they are not suitable for GOOSE and SV messages. Whereas the others exceed the target latency of 3 ms or do not comply with the standards, our design authenticates and encrypts real-time IEC 61850 data in less than 7 mu s-predictable latency-, and complies with IEC 62351:2020.This work was supported in part by the Ministerio de Economia y Competitividad of Spain under Project TEC2017-84011-R, in part by Fondo Europeo de Desarrollo Regional (FEDER) Funds through the Doctorados Industriales program under Grant DI-15-07857, and in part by the Department of Education, Linguistic Policy and Culture of the Basque Government through the Fund for Research Groups of the Basque University System under Grant IT978-16

A novel hybrid methodology to secure GOOSE messages against cyberattacks in smart grids

: IEC 61850 is emerging as a popular communication standard for smart grids. Standardized communication in smart grids has an unwanted consequence of higher vulnerability to cyber-attacks. Attackers exploit the standardized semantics of the communication protocols to launch different types of attacks such as false data injection (FDI) attacks. Hence, there is a need to develop a cybersecurity testbed and novel mitigation strategies to study the impact of attacks and mitigate them. This paper presents a testbed and methodology to simulate FDI attacks on IEC 61850 standard compliant Generic Object-Oriented Substation Events (GOOSE) protocol using real time digital simulator (RTDS) together with open-source tools such as Snort and Wireshark. Furthermore, a novel hybrid cybersecurity solution by the name of sequence content resolver is proposed to counter such attacks on the GOOSE protocol in smart grids. Utilizing the developed testbed FDI attacks in the form of replay and masquerade attacks on are launched and the impact of attacks on electrical side is studied. Finally, the proposed hybrid cybersecurity solution is implemented with the developed testbed and its effectiveness is demonstrated

Performance evaluation of IEC 61850 MMS messages under cybersecurity considerations

IEC 62351-4 standard is published to address cybersecurity vulnerabilities of IEC 61850 Manufacturing Message Specification (MMS) messages. This standard includes a set of cipher suites that are recommended for securing MMS messages. However, these are only a set of recommendations. There is no work in the literature that implements them on an IEC 61850 MMS message and reports the performances. In order to fill this importance knowledge gap, this short communication reports results of implementing cipher suites recommended by IEC 62351-4 on IEC 61850 messages. In addition to implementation details, real message exchanges are demonstrated with lab experiments. Finally, changing certificate and message sizes are reported. The results show that cipher suite selection is critical as some suites have 29.67 % smaller certificate size than others. The novelty of this short communication is showing details of IEC 62351 application and relevant changes on message sizes and structures of IEC 61850 MMS messages. There is no similar work or publication showing such procedures and results

ICT Technologies, Standards and Protocols for Active Distribution Network Automation and Management

The concept of active distribution network (ADN) is evolved to address the high penetration of renewables in the distribution network. To leverage the benefits of ADN, effective communication and information technology is required. Various communication standards to facilitate standard-based communication in distribution network have been proposed in literature. This chapter presents various communication standards and technologies that can be employed in ADN. Among various communication standards, IEC 61850 standard has emerged as the de facto standard for power utility automation. IEC 61850-based information modeling for ADN entities has also been presented in this chapter. To evaluate the performance of ADN communication architecture, performance metrics and performance evaluation tools have also been presented in this chapter

On the Detection of Cyber-Attacks in the Communication Network of IEC 61850 Electrical Substations

The availability of the data within the network communication remains one of the most critical requirement when compared to integrity and confidentiality. Several threats such as Denial of Service (DoS) or flooding attacks caused by Generic Object Oriented Substation Event (GOOSE) poisoning attacks, for instance, might hinder the availability of the communication within IEC 61850 substations. To tackle such threats, a novel method for the Early Detection of Attacks for the GOOSE Network Traffic (EDA4GNeT) is developed in the present work. Few of previously available intrusion detection systems take into account the specific features of IEC 61850 substations and offer a good trade-off between the detection performance and the detection time. Moreover, to the best of our knowledge, none of the existing works proposes an early anomaly detection method of GOOSE attacks in the network traffic of IEC 61850 substations that account for the specific characteristics of the network data in electrical substations. The EDA4GNeT method considers the dynamic behavior of network traffic in electrical substations. The mathematical modeling of the GOOSE network traffic first enables the development of the proposed method for anomaly detection. In addition, the developed model can also support the management of the network architecture in IEC 61850 substations based on appropriate performance studies. To test the novel anomaly detection method and compare the obtained results with available techniques, two use cases are used

Co-design of Security Aware Power System Distribution Architecture as Cyber Physical System

The modern smart grid would involve deep integration between measurement nodes, communication systems, artificial intelligence, power electronics and distributed resources. On one hand, this type of integration can dramatically improve the grid performance and efficiency, but on the other, it can also introduce new types of vulnerabilities to the grid. To obtain the best performance, while minimizing the risk of vulnerabilities, the physical power system must be designed as a security aware system. In this dissertation, an interoperability and communication framework for microgrid control and Cyber Physical system enhancements is designed and implemented taking into account cyber and physical security aspects. The proposed data-centric interoperability layer provides a common data bus and a resilient control network for seamless integration of distributed energy resources. In addition, a synchronized measurement network and advanced metering infrastructure were developed to provide real-time monitoring for active distribution networks. A hybrid hardware/software testbed environment was developed to represent the smart grid as a cyber-physical system through hardware and software in the loop simulation methods. In addition it provides a flexible interface for remote integration and experimentation of attack scenarios. The work in this dissertation utilizes communication technologies to enhance the performance of the DC microgrids and distribution networks by extending the application of the GPS synchronization to the DC Networks. GPS synchronization allows the operation of distributed DC-DC converters as an interleaved converters system. Along with the GPS synchronization, carrier extraction synchronization technique was developed to improve the system’s security and reliability in the case of GPS signal spoofing or jamming. To improve the integration of the microgrid with the utility system, new synchronization and islanding detection algorithms were developed. The developed algorithms overcome the problem of SCADA and PMU based islanding detection methods such as communication failure and frequency stability. In addition, a real-time energy management system with online optimization was developed to manage the energy resources within the microgrid. The security and privacy were also addressed in both the cyber and physical levels. For the physical design, two techniques were developed to address the physical privacy issues by changing the current and electromagnetic signature. For the cyber level, a security mechanism for IEC 61850 GOOSE messages was developed to address the security shortcomings in the standard

Secure Control and Operation of Energy Cyber-Physical Systems Through Intelligent Agents

The operation of the smart grid is expected to be heavily reliant on microprocessor-based control. Thus, there is a strong need for interoperability standards to address the heterogeneous nature of the data in the smart grid. In this research, we analyzed in detail the security threats of the Generic Object Oriented Substation Events (GOOSE) and Sampled Measured Values (SMV) protocol mappings of the IEC 61850 data modeling standard, which is the most widely industry-accepted standard for power system automation and control. We found that there is a strong need for security solutions that are capable of defending the grid against cyber-attacks, minimizing the damage in case a cyber-incident occurs, and restoring services within minimal time. To address these risks, we focused on correlating cyber security algorithms with physical characteristics of the power system by developing intelligent agents that use this knowledge as an important second line of defense in detecting malicious activity. This will complement the cyber security methods, including encryption and authentication. Firstly, we developed a physical-model-checking algorithm, which uses artificial neural networks to identify switching-related attacks on power systems based on load flow characteristics. Secondly, the feasibility of using neural network forecasters to detect spoofed sampled values was investigated. We showed that although such forecasters have high spoofed-data-detection accuracy, they are prone to the accumulation of forecasting error. In this research, we proposed an algorithm to detect the accumulation of the forecasting error based on lightweight statistical indicators. The effectiveness of the proposed algorithms was experimentally verified on the Smart Grid testbed at FIU. The test results showed that the proposed techniques have a minimal detection latency, in the range of microseconds. Also, in this research we developed a network-in-the-loop co-simulation platform that seamlessly integrates the components of the smart grid together, especially since they are governed by different regulations and owned by different entities. Power system simulation software, microcontrollers, and a real communication infrastructure were combined together to provide a cohesive smart grid platform. A data-centric communication scheme was selected to provide an interoperability layer between multi-vendor devices, software packages, and to bridge different protocols together

Machine Learning based Anomaly Detection for Cybersecurity Monitoring of Critical Infrastructures

openManaging critical infrastructures requires to increasingly rely on Information and Communi- cation Technologies. The last past years showed an incredible increase in the sophistication of attacks. For this reason, it is necessary to develop new algorithms for monitoring these infrastructures. In this scenario, Machine Learning can represent a very useful ally. After a brief introduction on the issue of cybersecurity in Industrial Control Systems and an overview of the state of the art regarding Machine Learning based cybersecurity monitoring, the present work proposes three approaches that target different layers of the control network architecture. The first one focuses on covert channels based on the DNS protocol, which can be used to establish a command and control channel, allowing attackers to send malicious commands. The second one focuses on the field layer of electrical power systems, proposing a physics-based anomaly detection algorithm for Distributed Energy Resources. The third one proposed a first attempt to integrate physical and cyber security systems, in order to face complex threats. All these three approaches are supported by promising results, which gives hope to practical applications in the next future.openXXXIV CICLO - SCIENZE E TECNOLOGIE PER L'INGEGNERIA ELETTRONICA E DELLE TELECOMUNICAZIONI - Elettromagnetismo, elettronica, telecomunicazioniGaggero, GIOVANNI BATTIST