More Like this

1. Real-world Cyber Security Demonstration for Networked Electric Drives

   https://doi.org/10.1109/JESTPE.2025.3550830  

   Yang, He; Yang, Bowen; Coshatt, Stephen; Li, Qi; Hu, Kun; Hammond, Bryan Cooper; Ye, Jin; Parasuraman, Ramviyas; Song, Wenzhan (January 2025, IEEE Journal of Emerging and Selected Topics in Power Electronics)

   In this paper, we present the design and implementation of a cyber-physical security testbed for networked electric drive systems, aimed at conducting real-world security demonstrations. To our knowledge, this is one of the first security testbeds for networked electric drives, seamlessly integrating the domains of power electronics and computer science, and cybersecurity. By doing so, the testbed offers a comprehensive platform to explore and understand the intricate and often complex interactions between cyber and physical systems. The core of our testbed consists of four electric machine drives, meticulously configured to emulate small-scale but realistic information technology (IT) and operational technology (OT) networks. This setup both provides a controlled environment for simulating a wide array of cyber attacks, and mirrors potential real-world attack scenarios with a high degree of fidelity. The testbed serves as an invaluable resource for the study of cyber-physical security, offering a practical and dynamic platform for testing and validating cybersecurity measures in the context of networked electric drive systems. As a concrete example of the testbed’s capabilities, we have developed and implemented a Python-based script designed to execute step-stone attacks over a wireless local area network (WLAN). This script leverages a sequence of target IP addresses, simulating a real-world attack vector that could be exploited by adversaries. To counteract such threats, we demonstrate the efficacy of our developed cyber-attack detection algorithms, which are integral to our testbed’s security framework. Furthermore, the testbed incorporates a real-time visualization system using InfluxDB and Grafana, providing a dynamic and interactive representation of networked electric drives and their associated security monitoring mechanisms. 

   more » « less
2. Recent Advances in Cyberattack Detection and Mitigation Techniques for Renewable Photovoltaic Distributed Energy CPS

   Jessica Whitaker and Danda B. Rawat (June 2023, Computing Conference)

   Cyberattacks targeted to the energy cyber-physical system (ECPS), also known as the smart grid, could interrupt the electricity supply with major ramifications. Attackers identify and exploit any vulnerable portion of the energy power grid, including the inverters with solar-powered photovoltaic (PV) panels. PV presents unique challenges as electricity consumers have also become providers of solar energy for utilities. As mandates require increased PV penetration across the world for positive environmental impacts, increased cyberattacks targeted at PV systems impact reliability and efficiency within the ECPS. The new technologies continuously being introduced to manage the ECPS and ensure bi-directional communications and energy flow between components also lead to more attack surfaces, system vulnerabilities, and heightened malicious attacks. Data integrity attacks are increasing within PV systems. In this paper, we present a survey of different methods that are proposed and explored for identifying and preventing cyberattacks targeted at PV systems. The attack detection methods include voltage control, data diodes, and voltage measurement algorithms. Furthermore, we present blockchain, cyber switching, and other attack mitigation techniques for PV systems. 

   more » « less
3. A Four-layer Cyber-physical Security Model for Electric Machine Drives considering Control Information Flow

   https://doi.org/10.1109/JESTPE.2024.3366089  

   Yang, Bowen; Yang, He; Ye, Jin (January 2024, IEEE Journal of Emerging and Selected Topics in Power Electronics)

   Despite the IEEE Power Electronics Society (PELS) establishing Technical Committee 10 on Design Methodologies with a focus on the cyber-physical security of power electronics systems, a holistic design methodology for addressing security vulnerabilities remains underdeveloped. This gap largely stems from the limited integration of computer science and power/control engineering studies in this interdisciplinary field. Addressing the inadequacy of unilateral cyber or control perspectives, this paper presents a novel four-layer cyber-physical security model specifically designed for electric machine drives. Central to this model is the innovative Control Information Flow (CIF) model, residing within the control layer, which serves as a pivotal link between the cyber layer’s vulnerable resources and the physical layer’s state-space models. By mapping vulnerable resources to control variable space and tracing attack propagation, the CIF model facilitates accurate impact predictions based on tainted control laws. The effectiveness and validity of this proposed model are demonstrated through hardware experiments involving two typical cyber-attack scenarios, underscoring its potential as a comprehensive framework for multidisciplinary security strategies. 

   more » « less
4. Physics-Informed Learning-based Attack Analytics for Electric Vehicle Charging Management Systems

   Perry, David; Mohammad, Haider; Kazmi, Kumail; Rahman, Mohammad Ashiqur; Shahriar, Hossain (July 2025, IEEE)

   This work introduces a novel physics-informed neural network (PINN)-based framework for modeling and optimizing false data injection (FDI) attacks on electric vehicle charging station (EVCS) networks, with a focus on centralized charging management system (CMS). By embedding the governing physical laws as constraints within the neural network’s loss function, the proposed framework enables scalable, real-time analysis of cyber-physical vulnerabilities. The PINN models EVCS dynamics under both normal and adversarial conditions while optimizing stealthy attack vectors that exploit voltage and current regulation. Evaluations on the IEEE 33-bus system demonstrate the framework’s capability to uncover critical vulnerabilities. These findings underscore the urgent need for enhanced resilience strategies in EVCS networks to mitigate emerging cyber threats targeting the power grid. Furthermore, the framework lays the groundwork for exploring a broader range of cyber-physical attack scenarios on EVCS networks, offering potential insights into their impact on power grid operations. It provides a flexible platform for studying the interplay between physical constraints and adversarial manipulations, enhancing our understanding of EVCS vulnerabilities. This approach opens avenues for future research into robust mitigation strategies and resilient design principles tailored to the evolving cybersecurity challenges in smart grid systems. 

   more » « less
5. COMPARISON OF DESIGN- AND DATACENTRIC METHODS FOR DISTRIBUTED ATTACK DETECTION IN CYBERPHYSICAL SYSTEMS

   Jennifer Leopold, Jennifer; McMillin, Bruce; Stiffler, Rachel; Lutes, Nathan (March 2020, Critical Infrastructure Protection XIV)

   Cyber-physical systems are vulnerable to a variety of cyber, physical and cyber-physical attacks. The security of cyber-physical systems can be enhanced beyond what can be achieved through firewalls and trusted components by building trust from observed and/or expected behaviors. These behaviors can be encoded as invariants. Information flows that do not satisfy the invariants are used to identify and isolate malfunctioning devices and cyber intrusions. However, the distributed architectures of cyber-physical systems often contain multiple access points that are physically and/or digitally linked. Thus, invariants may be difficult to determine and/or computationally prohibitive to check in real time. Researchers have employed various methods for determining the invariants by analyzing the designs of and/or data generated by cyber-physical systems such as water treatment plants and electric power grids. This chapter compares the effectiveness of detecting attacks on a water treatment plant using design-centric invariants versus data-centric rules, the latter generated using a variety of data mining methods. The methods are compared based on the maximization of true positives and minimization of false positives. 

   more » « less