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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Cyber-Resilient Automatic Generation Control for Systems of AC Microgrids

In this paper we propose a co-design of the secondary frequency regulation in systems of AC microgrids and its cyber security solutions. We term the secondary frequency regulator a Micro-Automatic Generation Control (μ AGC) for highlighting its same functionality as the AGC in bulk power systems. We identify sensory challenges and cyber threats facing the μ AGC. To address the sensory challenges, we introduce a new microgrid model by exploiting the rank-one deficiency property of microgrid dynamics. This model is used to pose an optimal μ AGC control problem that is easily implemented, because it does not require fast frequency measurements. An end-to-end cyber security solution to the False Data Injection (FDI) attack detection and mitigation is developed for the proposed μ AGC. The front-end barrier of applying off-the-shelf algorithms for cyber attack detection is removed by introducing a data-driven modeling approach. Finally, we propose an observer-based corrective control for an islanded microgrid and a collaborative mitigation scheme in systems of AC microgrids. We demonstrate a collaborative role of systems of microgrids during cyber attacks. Furthermore, the performance of the proposed cyber-resilient μ AGC is tested in a system of two networked microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Real-Time Testbed for Studying Cyberattacks and Defense in DER-integrated Smart Inverter Systems

In this paper, we propose a Hardware-in-the-Loop (HIL) simulation testbed suitable for the implementation and testing of realistic cyberattacks on grid-tied smart inverter systems integrated with Distributed Energy Resources (DER) that use the Distributed Network Protocol-3 (DNP3) protocol for communications between grid components. Specifically, our testbed combines a Real-Time Digital Simulator (RTDS) NovaCor device, outfitted with GNETx2 network interface cards, a gridtied DER topology implemented via the RTDS software package RSCAD, and a custom virtual network that emulates a man in the middle attacker. The Man-in-the-Middle (MITM) attacker captures DNP3 traffic and falsifies telemetry data in DNP3 packets to trigger unwarranted commands from a DNP3 controller that exploit smart inverter grid support functions. We choose DNP3 and implement grid support functions according to the IEEE Std. 1547-2018 mandated for the interconnection and interoperability of DER power systems with associated power components. Furthermore, we develop a protocol payload agnostic attack detection framework that leverages the round-trip time (RTT) anomalies between DNP3 requests and responses and can detect the presence of attacks without having to analyze the payload’s contents, while balancing trade-offs between false alarm counts, missed detections, and time to detection. To facilitate further research, we publicly release benign and attack network traffic exchanged between various sensors, controllers, and actuators in our grid-tied inverter testbed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Adaptive Cyber-Physical Resilience for Building Control Systems

The main goal of the project is to develop an AI-based process layer cybersecurity suite for detection, isolation and mitigation of cyber-attack effects on operation of building energy management systems (BEMS). The following constituent key technologies were developed under the program towards fulfilling the program objectives: (1) developed a high fidelity BEMS testbed for generation of training data and validation of developed technologies; (2) developed a physics informed ML based attack detection and localization module (ADL) capable of detecting high impact stealthy attacks (HISA - attacks causing 30% energy utilization but no immediate visible impact otherwise) with 98% accuracy; (3) developed a methodology to determine ’representative days’ to limit the data required for training; (4) developed a virtual sensing system that can reconstruct affected sensors with 10% error for the same HISA set; (5) developed a resilient model predictive control system that can continue operation of the BEMS without jeopardizing stability for the HISA set; and (6) integrated and deployed all the constituent modules and demonstrated the efficacy of the technology in real-time in a hardware in loop simulation.

42 ENGINEERING↗
DOE OSTI2025

Automated Programmable Logic Controller Memory Forensics Using RGB Image Analysis and Deep Learning

The introduction of Industry 4.0 and Internet-based technologies has enhanced industrial control system operations but have inadvertently increased their vulnerabilities to cyber attacks. When an industrial control system is compromised, security analysts need to identify the root cause quickly to start the recovery process and develop mitigation strategies. Memory forensics is critical in the incident analysis process to ascertain what occurred. Approaches for analyzing the persistent memory in industrial control devices are limited and almost nonexistent for volatile memory. This chapter proposes an automated methodology for programmable logic controller memory dump analysis using computer vision and deep learning techniques. The methodology converts the sequences of bytes in a programmable logic controller memory dump to red-green-blue pixels and employs a deep learning model that learns the underlying patterns and features of pre-labeled forensic artifacts in images and segments them into distinct regions. The trained model is employed to automatically segment new memory images and identify forensic artifacts. Evaluation of the methodology on a Schneider Electric Modicon M221 programmable logic controller under code injection and code modification attacks demonstrates its ability to detect attack artifacts in memory dumps.

Asmar Awad, Rima [ORNL] (ORCID:0000000233407742)↗
DOE OSTI2023

Maximum-impact Adversary Design for Network-based Control System: A Case Study on Grid-interactive Efficient Buildings

The Internet of Things (IoT) technology has dramatically improved the efficiency of today's building operation and management. By connecting controllable devices into a communication network, control signals can be easily passed to the devices, and operating status can be acquired from measurable ends with minimal effort. However, this all-connected configuration could also expose the network-based control system (NBCS) to malicious actions, such as cyberattacks. One of the common NBCSs is the building automation system. With the promotion of grid-interactive efficient buildings (GEBs), there has been increasing attention on securing the buildings from the network perspective. This research proposes a maximum-impact adversary design framework so that the adversary can provide the most adversarial impact on the controlled system while remaining stealthy. The proposed framework is numerically demonstrated on a network-based building energy and control system. The building energy system is built in a Modelica-based simulation environment and controlled by the state-of-the-art ASHRAE Guideline 36 control sequences. The control commands at the supervisory level, generated from the Guideline 36 controller, are assumed to be sent to local devices through communication networks using the BACnet protocol. Simulation results show that the proposed maximum-impact adversary on such a system can stealthily affect the building system's performance to its maximum extent. It is anticipated that results can be used by researchers and practitioners in the building automation industry to design efficient and robust cyber-attack detection algorithms, especially for stealthy attacks.

Chu, Mengyuan↗
DOE OSTI2021

Hardware-in-the-Loop Testbed for Cyber-Physical Security of Photovoltaic Farms

In the last decades, modem grids with distributed energy resources, such as photovoltaic (PV) farms, are increasingly vulnerable to cyber-attacks that seriously affect the stability and performance of the power system. While cyber-physical security of smart grids is extensively studied, most of the existing work focuses on the grid level and neglects the modeling and features of device-level power electronics converters (PECs). Furthermore, establishing a high-fidelity simulation testbed that can simulate harmonic frequencies of the PV farm is in urgent need. In this paper, a high-fidelity and real-time hardware-in- the-loop testbed is built to simulate the harmonics of power electronics converters for cyber-physical security of PEC-enabled PV farms. Based on this testbed, the impact of typical cyber-attacks and physical faults on the PV converter can be analyzed, thus providing a foundation for cyber-attack detection, root cause diagnosis, and resilient control to mitigate the adverse effects of cyber-attacks.

14 SOLAR ENERGY↗
DOE OSTI2022

HIDES (Hybrid Intrusion Detection for Energy Systems) [SWR-19-65]

Hybrid Intrusion Detection for Energy Systems (HIDES) is a conglomerate detector that incorporates three major components: (1) Signature-based detection that involves information technology (IT) related anomaly detection rules, (2) Behavior-based detection that utilizes specially crafted rulesets for detecting abnormal behavior regarding the power system's supervisory control and data acquisition (SCADA) communication streams, and (3) Learning-based detection that combines SCADA communication with phasor measurements to identify otherwise non-detectible attack vectors. The software itself interconnects intrusion detection engine (IDE) enabling communications between the generated OT and IT alerts with phasor measurements for the learning-based detection system. The resulting detection system can detect a wide array of IT, OT, and physical attacks in a real-time setting.

Rivera, Joshua↗
DOE OSTI2026

Evaluating lightweight unsupervised online IDS for masquerade attacks in CAN

Vehicular controller area networks (CANs) are susceptible to masquerade attacks by malicious adversaries. In masquerade attacks, adversaries silence a targeted ID and then send malicious frames with forged content at the expected timing of benign frames. As masquerade attacks could seriously harm vehicle functionality and are the stealthiest attacks to detect in CAN, recent work has devoted attention to compare frameworks for detecting masquerade attacks in CAN. However, most existing works report offline evaluations using CAN logs already collected using simulations that do not comply with the domain’s real-time constraints. Here we contribute to advance the state of the art by presenting a comparative evaluation of four different non-deep learning (DL)-based unsupervised online intrusion detection systems (IDS) for masquerade attacks in CAN. Our approach differs from existing comparative evaluations in that we analyze the effect of controlling streaming data conditions in a sliding window setting. In doing so, we use realistic masquerade attacks being replayed from the ROAD dataset. We show that although evaluated IDS are not effective at detecting every attack type, the method that relies on detecting changes in the hierarchical structure of clusters of time series produces the best results at the expense of higher computational overhead. We discuss limitations, open challenges, and how the evaluated methods can be used for practical unsupervised online CAN IDS for masquerade attacks.

Anomaly detection↗
DOE OSTI2021

Metagames and Hypergames for Deception-Robust Control

Cyber-physical systems (CPSs) consist of computing and communication devices integrated with physical components such as sensors and actuators. Increasing connectivity to the Internet for remote monitoring and control has made CPSs more vulnerable to deliberate attacks, which are distinctly different from random perturbations in the system. This provides a way for purely cyber attacks to have physical consequences. Stuxnet is a prominent example of such an attack, one in which the malware acted over an extended period of time while deliberately remaining undetected. Such attacks can be described as Advanced Persistent Threats (APTs) -- long-term, stealthy attacks. Here, we extend our previous work on hypergames to develop defender strategies that are robust to deception and do not rely on attack detection. We prove that the defender can bound the attacker payoff with these strategies even when the attacker can choose between different attack modes, and we numerically demonstrate our approach on a realistic building control system. Finally, we discuss next steps in extending this approach towards an operational capability.

hypergames, cyber-physical systems, robust control↗
DOE OSTI2025

Secure and Resilient Operations Using Open-Source Distributed Systems Platform (OpenDSP)

The goal of this project is to identify and address cybersecurity gaps by developing a multi-layer multi-channel cyber-physical defense and survival mechanism for operating distribution networks with high penetration of solar / inverter-based resource (IBR) / distributed energy resource (DER). The proposed security enhancements are built upon the distributed framework and solution architecture for both information technology (IT) and operational technology (OT) systems. The technical solutions consist of two composite functionalities and six layers: proactive defense (vulnerability assessment, communication protection, and attack detection, as layers 1-3), and adaptive self-healing (attack-resilient control, adaptive recovery, and resilient survival, as layers 4-6). These layers, built on and extended from DHS CISA Cyber Framework, establish an integrated and robust cybersecurity framework for operating large-scale distribution networks.

14 SOLAR ENERGY↗
DOE OSTI2022

Simulated 5g Network Traffic Dataset

This is a dataset of 5G network traffic for use with machine learning tools to benchmark attack detection capabilities for multiple different models. The dataset contains simulated normal and attack 5G network traffic. There is no software in this dataset, only simulated network traffic data.

Anderson, MatthewW↗
DOE OSTI2024

Beyond Binary: Automated PLC Memory Forensics through RGB Image Analysis and Deep Learning

The introduction of Industry 4.0 and the evolution of industrial control systems (ICS) to adopt Internet-based technologies enhanced productivity, but have inadvertently increased their vulnerability to cyber-based malicious attacks. When an ICS system is compromised, security analysts need to identify the root cause quickly to start the recovery process and develop mitigation strategies to safeguard against future instances. Memory forensics is critical in the analysis process to ascertain what occurred. To date, approaches to analyze the persistent memory in ICS devices are limited, and almost nonexistent for volatile memory. This paper proposes an automated methodology, COMA, for PLC memory dump analysis using computer vision and deep learning techniques. Specifically, COMA converts the sequences of bytes in a PLC memory dump to RGB pixels and creates a deep learning model that learns the underlying patterns and features of pre-labeled forensic artifacts in images and segments them into distinct regions. COMA then uses the trained model to automatically segment new memory images and extract forensic artifacts. We evaluate COMA on a Schneider Electric Modicon M221 PLC involving two cyber-based attack scenarios: (i) code injection and (ii) code modification. The empirical results show that COMA can successfully detect attack artifacts in memory dumps in both scenarios.

Asmar Awad, Rima↗
DOE OSTI2022

Detection of Cyber Attacks in Grid-tied PV Systems Using Dynamic Watermarking

This paper presents of an active detection scheme for detecting cyber attacks on sensors controlling a grid-tied PV systems. Several cyber vulnerabilities in Grid tied PV Systems are discussed. The defense mechanism introduces a private (secret) watermarking signal into the control inputs of the grid-tied inverter system. This will enable the detection of any malicious manipulation of sensor measurements. Based on the measured data, two statistical tests are conducted to identify anomalies in the system using the presence of the watermarking signal. It shown that when a sensor data is compromised and/or replaced by a pre-recorded healthy signal, both test 1 and 2 exhibit high values indicating a possible malicious activity. The robustness of the proposed algorithm is tested and validated with several attack scenarios on a grid tied PV system. Select results from an experimental setup are discussed.

Ibrahim, Hasan↗
DOE OSTI2022

Detecting CAN Masquerade Attacks with Signal Clustering Similarity

Vehicular Controller Area Networks (CANs) are susceptible to cyber attacks of different levels of sophistication. Fabrication attacks are the easiest to administer—an adversary simply sends (extra) frames on a CAN—but also the easiest to detect because they disrupt frame frequency. To overcome time-based detection methods, adversaries must administer masquerade attacks by sending frames in lieu of (and therefore at the expected time of) benign frames but with malicious payloads. Research efforts have proven that CAN attacks, and masquerade attacks in particular, can affect vehicle functionality. Examples include causing unintended acceleration, deactivation of vehicle’s brakes, as well as steering the vehicle. We hypothesize that masquerade attacks modify the nuanced correlations of CAN signal time series and how they cluster together. Therefore, changes in cluster assignments should indicate anomalous behavior. We confirm this hypothesis by leveraging our previously developed capability for reverse engineering CAN signals (i.e., CAN-D [Controller Area Network Decoder]) and focus on advancing the state of the art for detecting masquerade attacks by analyzing time series extracted from raw CAN frames. Specifically, we demonstrate that masquerade attacks can be detected by computing time series clustering similarity using hierarchical clustering on the vehicle’s CAN signals (time series) and comparing the clustering similarity across CAN captures with and without attacks. We test our approach in a previously collected CAN dataset with masquerade attacks (i.e., the ROAD dataset) and develop a forensic tool as a proof of concept to demonstrate the potential of the proposed approach for detecting CAN masquerade attacks.

Moriano Salazar, Pablo↗
DOE OSTI2023

Data-driven Vulnerability Analysis of Networked Pipeline System

This paper introduces an attack generation framework for evaluating the vulnerability of nonlinear networked pipeline systems. The vulnerability analysis is formulated as determining the presence of feasible attack sets, defined by boundary functions representing the effectiveness and stealthiness of attack signals with respect to the objective and attack detection module. The framework utilizes three data-driven models, including two discriminative models that learn the boundary functions and a generative model that produces elements of the feasible attack set. A new loss function ensures successful attack generation with high probability.

03 NATURAL GAS↗
DOE OSTI2025

An Advanced Cyber-Physical System Security Testbed for Substation Automation

A Cyber-Physical System (CPS) testbed serves as a powerful platform for testing and validating cyber intrusion detection and mitigation strategies in substations. This study presents the design and development of a CPS testbed that can effectively assess the real-time dynamics of a substation. Cyber attacks exploiting IEC 61850-based SV and GOOSE protocols are demonstrated using the testbed, along with an analysis on attack detection. Realistic timing measurements are obtained, and the time frames for deploying detection and mitigation strategies are evaluated.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Online and Offline Identification of False Data Injection Attacks in Battery Sensors Using a Single Particle Model

The cells in battery energy storage systems are monitored, protected, and controlled by battery management systems whose sensors are susceptible to cyberattacks. False data injection attacks (FDIAs) targeting batteries’ voltage sensors affect cell protection functions and the estimation of critical battery states like the state of charge (SoC). Inaccurate SoC estimation could result in battery overcharging and over discharging, which can have disastrous consequences on grid operations. This paper proposes a three-pronged online and offline method to detect, identify, and classify FDIAs corrupting the voltage sensors of a battery stack. To accurately model the dynamics of the series-connected cells a single particle model is used and to estimate the SoC, the unscented Kalman filter is employed. FDIA detection, identification, and classification was accomplished using a tuned cumulative sum (CUSUM) algorithm, which was compared with a baseline method, the chi-squared error detector. Online simulations and offline batch simulations were performed to determine the effectiveness of the proposed approach. Throughout the batch simulations, the CUSUM algorithm detected attacks, with no false positives, in 99.83% of cases, identified the corrupted sensor in 97% of cases, and determined if the attack was positively or negatively biased in 97% of cases.

25 ENERGY STORAGE↗