Cyber-physical systems have behaviour that crosses domain boundaries during events such as planned operational changes and malicious disturbances. Traditionally, the cyber and physical systems are monitored separately and use very different toolsets and analysis paradigms. The security and privacy of these cyber-physical systems requires improved understanding of the combined cyber-physical system behaviour and methods for holistic analysis. Therefore, the authors propose leveraging clustering techniques on cyber-physical data from smart grid systems to analyse differences and similarities in behaviour during cyber-, physical-, and cyber-physical disturbances. Since clustering methods are commonly used in data science to examine statistical similarities in order to sort large datasets, these algorithms can assist in identifying useful relationships in cyber-physical systems. Through this analysis, deeper insights can be shared with decision-makers on what cyber and physical components are strongly or weakly linked, what cyber-physical pathways are most traversed, and the criticality of certain cyber-physical nodes or edges. This paper presents several types of clustering methods for cyber-physical graphs of smart grid systems and their application in assessing different types of disturbances for informing cyber-physical situational awareness. The collection of these clustering techniques provide a foundational basis for cyber-physical graph interdependency analysis.
As the electric grid becomes increasingly cyber-physical, it is important to characterize its inherent cyber-physical interdepedencies and explore how that characterization can be leveraged to improve grid operation. It is crucial to investigate what data features are transferred at the system boundaries, how disturbances cascade between the systems, and how planning and/or mitigation measures can leverage that information to increase grid resilience. In this paper, we explore several numerical analysis and graph decomposition techniques that may be suitable for modeling these cyber-physical system interdependencies and for understanding their significance. An augmented WSCC 9-bus cyber-physical system model is used as a small use-case to assess these techniques and their ability in characterizing different events within the cyber-physical system. These initial results are then analyzed to formulate a high-level approach for characterizing cyber-physical interdependencies.
The harmonized automatic relay mitigation of nefarious intentional events (HARMONIE) special protection scheme (SPS) was developed to provide adaptive, cyber-physical response to unpredictable disturbances in the electric grid. The HARMONIE-SPS methodology includes a machine learning classification framework that analyzes real time cyber-physical data and determines if the system is in normal conditions, cyber disturbance, physical disturbance, or cyber-physical disturbance. This classification then informs response, if needed and/or suitable, and included cyber-physical corrective actions. Beyond standard power system mitigations, a few novel approaches were developed that included a consensus algorithm-based relay voting scheme, an automated power system triggering condition and corrective action pairing algorithm, and a cyber traffic routing optimization algorithm. Both the classification and response techniques were tested within a newly integrated emulation environment composed of a real-time digital simulator (RTDS) and SCEPTRE™. This report details the HARMONIE-SPS methodology, highlighting both the classification and response techniques, and the subsequent testing results from the emulation environment.
The electric grid has undergone rapid, revolutionary changes in recent years; from the addition of advanced smart technologies to the growing penetration of distributed energy resources (DERs) to increased interconnectivity and communications. However, these added communications, access interfaces, and third-party software to enable autonomous control schemes and interconnectivity also expand the attack surface of the grid. To address the gap of DER cybersecurity and secure the grid-edge to motivate a holistic, defense-in-depth approach, a proactive intrusion detection and mitigation system (PIDMS) device was developed to secure PV smart inverter communications. The PIDMS was developed as a distributed, flexible bump-in-the-wire (BITW) solution for protecting PV smart inverter communications. Both cyber (network traffic) and physical (power system measurements) are processed using network intrusion monitoring tools and custom machinelearning algorithms for deep packet analysis and cyber-physical event correlation. The PIDMS not only detects abnormal events but also deploys mitigations to limit or eliminate system impact; the PIDMS communicates with peer PIDMSs at different locations using the MQTT protocol for increased situational awareness and alerting. The details of the PIDMS methodology and prototype development are detailed in this report as well as the evaluation results within a cyber-physical emulation environment and subsequent industry feedback.
Unpredictable disturbances with dynamic trajectories such as extreme weather events and cyber attacks require adaptive, cyber-physical special protection schemes to mitigate cascading impact in the electric grid. A harmonized automatic relay mitigation of nefarious intentional events (HARMONIE) special protection scheme (SPS) is being developed to address that need. However, for evaluating the HARMONIE-SPS performance in classifying system disturbances and mitigating consequences, a cyber-physical testbed is required to further development and validate the methodology. In this paper, we present a design for a co-simulation testbed leveraging the SCEPTRE™ platform and the real-time digital simulator (RTDS). The integration of these two platforms is detailed, as well as the unique, specific needs for testing HARMONIE-SPS within the environment. Results are presented from tests involving a WSCC 9-bus system with different load shedding scenarios with varying cyber-physical impact.
The electric grid is becoming increasingly cyber-physical with the addition of smart technologies, new communication interfaces, and automated grid-support functions. Because of this, it is no longer sufficient to only study the physical system dynamics, but the cyber system must also be monitored as well to examine cyber-physical interactions and effects on the overall system. To address this gap for both operational and security needs, cyber-physical situational awareness is needed to monitor the system to detect any faults or malicious activity. Techniques and models to understand the physical system (the power system operation) exist, but methods to study the cyber system are needed, which can assist in understanding how the network traffic and changes to network conditions affect applications such as data analysis, intrusion detection systems (IDS), and anomaly detection. In this paper, we examine and develop models of data flows in communication networks of cyber-physical systems (CPSs) and explore how network calculus can be utilized to develop those models for CPSs, with a focus on anomaly and intrusion detection. This provides a foundation for methods to examine how changes to behavior in the CPS can be modeled and for investigating cyber effects in CPSs in anomaly detection applications.
Traditional protective relay voting schemes utilize simple logic to achieve confidence in relay trip actions. However, the smart grid is rapidly evolving and there are new needs for a next-generation relay voting scheme. In such new schemes, aspects such as inter-relay relationships and out-of-band data can be included. In this work, we explore the use of consensus algorithms and how they can be utilized for groups of relays to vote on system protection actions and also reach consensus on the values of variables in the system. A proposed design is explored with a simple case study with two different scenarios, including simulation in PowerWorld Simulator, to demonstrate the consensus algorithm benefits and future directions are discussed.
The electric grid is rapidly being modernized with novel technologies, adaptive and automated grid-support functions, and added connectivity with internet-based communications and remote interfaces. These advancements render the grid increasingly 'smart' and cyber-physical, but also broaden the vulnerability landscape and potential for malicious, cascading disturbances. The grid must be properly defended with security mechanisms such as intrusion detection systems (IDSs), but these tools must account for power system behavior as well as network traffic to be effective. In this paper, we present a cyber-physical IDS, the proactive intrusion detection and mitigation system (PIDMS), that analyzes both cyber and physical data streams in parallel, detects intrusion, and deploys proactive response. We demonstrate the PIDMS with an exemplar case study exploring a packet replay attack scenario focused on photovoltaic inverter communications; the scenario is tested with an emulated, cyber-physical grid environment with hardware-in-the-loop inverters.
Recent trends in the growth of distributed energy resources (DER) in the electric grid and newfound malware frameworks that target internet of things (IoT) devices is driving an urgent need for more reliable and effective methods for intrusion detection and prevention. Cybersecurity intrusion detection systems (IDSs) are responsible for detecting threats by monitoring and analyzing network data, which can originate either from networking equipment or end-devices. Creating intrusion detection systems for PV/DER networks is a challenging undertaking because of the diversity of the attack types and intermittency and variability in the data. Distinguishing malicious events from other sources of anomalies or system faults is particularly difficult. New approaches are needed that not only sense anomalies in the power system but also determine causational factors for the detected events. In this report, a range of IDS approaches were summarized along with their pros and cons. Using the review of IDS approaches and subsequent gap analysis for application to DER systems, a preliminary hybrid IDS approach to protect PV/DER communications is formed in the conclusion of this report to inform ongoing and future research regarding the cybersecurity and resilience enhancement of DER systems.
Special protection schemes (SPSs) safeguard the grid by detecting predefined abnormal conditions and deploying predefined corrective actions. Utilities leverage SPSs to maintain stability, acceptable voltages, and loading limits during disturbances. However, traditional SPSs cannot defend against unpredictable disturbances. Events such as cyber attacks, extreme weather, and electromagnetic pulses have unpredictable trajectories and require adaptive response. Therefore, we propose a harmonized automatic relay mitigation of nefarious intentional events (HARMONIE)-SPS that learns system conditions, mitigates cyber-physical consequences, and preserves grid operation during both predictable and unpredictable disturbances. In this paper, we define the HARMONIE-SPS approach, detail progress on its development, and provide initial results using a WSCC 9-bus system.
The penetration of Internet-of-Things (IoT) devices in the electric grid is growing at a rapid pace; from smart meters at residential homes to distributed energy resource (DER) system technologies such as smart inverters, various devices are being integrated into the grid with added connectivity and communications. Furthermore, with these increased capabilities, automated grid-support functions, demand response, and advanced communication-assisted control schemes are being implemented to improve the operation of the grid. These advancements render our power systems increasingly cyber-physical. It is no longer sufficient to only focus on the physical interactions, especially when implementing cybersecurity mechanisms such as intrusion detection systems (IDSs) and mitigation schemes that need to access both cyber and physical data. This new landscape necessitates novel methods and technologies to successfully interact and understand the overall cyber-physical system. Specifically, this paper will investigate the need and definition of cyber-physical observability for the grid.
As the power grid incorporates increasing amounts of distributed energy resources (DER) that provide new generation sources, new opportunities are created for improving operation of the grid while large challenges also arise for preserving grid reliability and security. To improve grid performance, DERs can be utilized to provide important support functionality, such as supporting frequency and voltage levels, especially if they are assisted by communication schemes as part of an advanced distribution management system (ADMS). Unfortunately, such connectivity and grid support functionality also creates additional cyber security risk with the potential for degradation of grid services, especially under conditions with high amounts of distributed generation. This paper will first discuss the communications needed by DERs to support system and interoperability objectives, as well as the security requirements and impact of securing these communications. Some common security mechanisms are discussed in relation to DERs, and a simulated 15-bus model of a distribution feeder is used to demonstrate aspects of the DER communications and impact to grid performance. These results help to advance understanding of the benefits, requirements, and mechanisms for securely implementing DER communications while ensuring that grid reliability is maintained.
The integration of communication-enabled grid-support functions in distributed energy resources (DER) and other smart grid features will increase the U.S. power grid's exposure to cyber-physical attacks. Unwanted changes in DER system data and control signals can damage electrical infrastructure and lead to outages. To protect against these threats, intrusion detection systems (IDSs) can be deployed, but their implementation presents a unique set of challenges in industrial control systems (ICSs), New approaches need to be developed that not only sense cyber anomalies, but also detect undesired physical system behaviors. For DER systems, a combination of cyber security data and power system and control information should be collected by the IDS to provide insight into the nature of an anomalous event. This allows joint forensic analysis to be conducted to reveal any relationships between the observed cyber and physical events. In this paper, we propose a hybrid IDS approach that monitors and evaluates both physical and cyber network data in DER systems, and present a series of scenarios to demonstrate how our approach enables the cyber-physical IDS to achieve more robust identification and mitigation of malicious events on the DER system.
This document will detail a test procedure, involving bench and emulation testing, for the Module OT device developed for the joint NREL-SNL DOE CEDS project titled "Modular Security Apparatus for Managing Distributed Cryptography for Command & Control Messages on Operational Technology (OT) Networks." The aim of this document is to create the testing and evaluation protocol for the module for lab-level testing; this includes checklists and experiments for information gathering, functional testing, cryptographic implementation, public key infrastructure, key exchange/authentication, encryption, and implementation testing in the emulation environment.
There are differences in how cyber-attack, sabotage, or discrete component failure mechanisms manifest within power plants and what these events would look like within the control room from an operator's perspective. This research focuses on understanding how a cyber event would affect the operation of the plant, how an operator would perceive the event, and if the operator's actions based on those perceptions will allow him/her to maintain plant safety. This research is funded as part of Sandia's Laboratory Directed Research and Development (LDRD) program to develop scenarios with cyber induced failure of plant systems coupled with a generic pressurized water reactor plant training simulator. The cyber scenario s w ere developed separately and injected into the simulator operational state to simulate an attack. These scenarios will determine if Nuclear Power Plant (NPP) operators can 1) recognize that the control room indicators were presenting incorrect or erroneous information and 2) take appropriate actions to keep the plant safe. This will also provide the opportunity to assess the operator cognitive workload during such events and identify where improvements might be made. This paper will review results of a pilot study run with NPP operators to investigate performance under various cyber scenarios. The discussion will provide an overview of the approach, scenario selection, metrics captured, resulting insights into operator actions and plant response to multiple scenarios of the NPP system.