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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Systematic planning of moving target defence for maximising detection effectiveness against false data injection attacks in smart grid

Abstract Moving target defence (MTD) has been gaining traction to thwart false data injection attacks against state estimation (SE) in the power grid. MTD actively perturbs the reactance of transmission lines equipped with distributed flexible AC transmission system (D‐FACTS) devices to falsify the attacker's knowledge about the system configuration. However, the existing literature has not systematically studied what influences the detection effectiveness of MTD and how it can be improved based on the topology analysis. These problems are tackled here from the perspective of an MTD plan in which the D‐FACTS placement is determined. We first exploit the relation between the rank of the composite matrix and the detecting effectiveness. Then, we rigorously derive upper and lower bounds on the attack detecting probability of MTDs with a given rank of the composite matrix. Furthermore, we analyse existing planning methods and highlight the importance of bus coverage by D‐FACTS devices. To improve the detection effectiveness, we propose a novel graph theory–based planning algorithm to retain the maximum rank of the composite matrix while covering all necessary buses. Comparative results on multiple systems show the high detecting effectiveness of the proposed algorithm in both DC‐ and AC‐SE.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1977

Pre-visual detection of stress in pine forests

Pre-visual, or early, detection of forest stress with particular reference to detection of attacks by pine bark beetles is discussed. Preliminary efforts to obtain early detection of attacks by pine bark beetles, using MSS data from the ERIM M-7 scanner, were not sufficiently successful to demonstrate an operational capability, but indicate that joint processing of the 0.71 to 0.73, 2.00 to 2.60, and 9.3 to 11.7 micrometer bands holds some promise. Ratio processing of transformed data from the 0.45 to 0.52, 1.55 to 2.60, and 4.5 to 5.5 or 9.3 to 11.7 micrometer regions appears even more promising.

Olson, C. E., Jr.↗
DOE OSTI2021

Sequential Perturbation-based FDIA Detection using DERs for Unbalanced Distribution System

The power distribution system with its reliance on automated monitoring and control infrastructure makes it a complicated cyber-physical system and vulnerable to various cyber-attacks. This paper proposes a moving target defense inspired false data injection attack (FDIA) detection mechanism for an unbalanced distribution system. In the proposed grid diagnosis service framework, the distribution system operator judiciously manipulates power outputs of a subset of inverter-based distribution energy resources (DERs) to create secret low magnitude voltage perturbations which are inconsequential to the normal operation of the grid. A mixed-integer-linear-programming algorithm is developed to select the optimal set of DERs that can create a voltage perturbation signal of the required magnitude at each sensor location at a minimum cost. Then, a sequential detector is applied that detects for the FDIA as measurements are received from individual sensors. The performance of the proposed perturbation-based FDIA detection framework is demonstrated via simulation of the IEEE 123 bus test system.

Attack Detection↗
DOE OSTI2024

Building Intelligence with Layered Defense Using Security-Constrained Optimization and Security Risk Detection (BUILD-SOS): A Probabilistic Approach

In this project, we employ a layered protection strategy incorporating advanced optimization and detection techniques using a probabilistic approach. The probabilistic approach is not only applied when detecting cyber attacks, but also incorporated in control strategies, which greatly increases the attacking difficulties. Hackers need to understand both probabilistic detection algorithms and uncertainty modeling methods in control in order to execute any effective attacks. The end-to-end solutions enable us to provide Building Intelligence with Layered Defense using Security-Constrained Optimization and Security Risk Detection (BUILD-SOS).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Online Dynamic Cyber-Attack Diagnosis in Power Electronics Systems Based on Few-Shot Learning

With increasing exposure to software-based sensing and control, power electronics systems are facing higher risks of cyber-physical attacks. To ensure system stability and minimize potential economic losses, it is critical to monitor the operating states and detect those attacks at the early stage. However, anomaly detection and diagnosis of attacks are still challenging, especially when labeled anomaly data is difficult or even infeasible to obtain. To overcome this problem, we propose a Few-Shot Learning (FSL) based approach for cyber-attack diagnosis leveraging the waveform data. To the best of our knowledge, this work is the first attempt at leveraging FSL for cyber-attack diagnosis in power electronics systems. Extensive experimental results demonstrate that our proposed approach can achieve comparable diagnosis accuracy with the state-of-the-art data-driven methods using less than 0.04% of the training samples.

Li, Qi↗
DOE OSTI2025

A Graph-Net with Node Embeddings to Detect False Data Injection Attacks in Photovoltaic Systems

Distributed energy resources (DER) contribute to the operational stability of the larger power grid both at utility-scale as well as commercial and residential scales in aggregated forms. These DER in-turn are susceptible to increasing cyber threats. An adversary can plug into the same local network that a field photovoltaic (PV) system uses to interconnect its data loggers and inverters and manipulate certain measurements collected from the network or trick existing irradiance and inverter readings through false data injection attacks (FDIA). Control routines that rely on these measurements can propagate the false data, impacting critical decisions that result in a suboptimal operation or even cause intentional harm leading to inverter-tripping or unscheduled loads that need to be shed. To detect FDIA in PV systems, the paper introduces an attention-based graph neural network with node embeddings and applied it to a simple prototypical DC-coupled microgrid with PV, energy storage, and load. The algorithm shows a detection accuracy of up to 98.95%. The proposed FDIA detection technique will provide micro-grid operators with an effective method to safeguard their systems, guaranteeing the secure and reliable operation.

Parvez, Imtiaz [Utah Valley University]↗
DOE OSTI2023

A critical review of cyber-physical security for building automation systems

Modern Building Automation Systems (BASs), as the brain that enable the smartness of a smart building, often require increased connectivity both among system components as well as with outside entities, such as the cloud, to enable low-cost remote management, optimized automation via outsourced cloud analytics, and increased building-grid integrations. As smart buildings move towards open communication technologies, providing access to BASs through the building's intranet, or even remotely through the Internet, has become a common practice. However, increased connectivity and accessibility come with increased cyber security threats. BASs were historically developed as closed environments with limited cyber-security considerations. As a result, BASs in many buildings are vulnerable to cyber-attacks that may cause adverse consequences, such as occupant discomfort, excessive energy usage, and unexpected equipment downtime. Therefore, there is a strong need to advance the state-of-the-art in cyber-physical security for BASs and provide practical solutions for attack mitigation in buildings. However, an inclusive and systematic review of BAS vulnerabilities, potential cyber-attacks with impact assessment, detection & defense approaches, and cyber resilient control strategies is currently lacking in the literature. This review paper fills the gap by providing a comprehensive up-to-date review of cyber-physical security for BASs at three levels in commercial buildings: management level, automation level, and field level. The general BASs vulnerabilities and protocol-specific vulnerabilities for the four dominant BAS protocols (i.e., BACnet, KNX, LonWorks, and Modbus) are reviewed, followed by a discussion on four attack targets and seven potential attack scenarios. Furthermore, the impact of cyber-attacks on BASs is summarized as signal corruption, signal delaying, and signal blocking. The typical cyber-attack detection and defense approaches are identified at the three levels. Cyber resilient control strategies for BASs under attack are categorized into passive and active resilient control schemes. Open challenges and future opportunities are finally discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Systems and methods for detecting and mitigating cyber attacks on power systems comprising distributed energy resources

Extensive deployment of interoperable distributed energy resources (DER) on power systems is increasing the power system cybersecurity attack surface. National and jurisdictional interconnection standards require DER to include a range of autonomous and commanded grid-support functions which can drastically influence power quality, voltage, and the generation-load balance. Investigations of the impact to the power system in scenarios where communications and operations of DER are controlled by an adversary show that each grid-support function exposes the power system to distinct types and magnitudes of risk. The invention provides methods for minimizing the risks to distribution and transmission systems using an engineered control system which detects and mitigates unsafe control commands.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Systems and methods for detecting and mitigating cyber attacks on power systems comprising distributed energy resources

Extensive deployment of interoperable distributed energy resources (DER) on power systems is increasing the power system cybersecurity attack surface. National and jurisdictional interconnection standards require DER to include a range of autonomous and commanded grid-support functions which can drastically influence power quality, voltage, and the generation-load balance. Investigations of the impact to the power system in scenarios where communications and operations of DER are controlled by an adversary show that each grid-support function exposes the power system to distinct types and magnitudes of risk. The invention provides methods for minimizing the risks to distribution and transmission systems using an engineered control system which detects and mitigates unsafe control commands.

Johnson, Jay Tillay↗
DOE OSTI2022

Two-Stage Optimization Framework for Detecting and Correcting Parameter Cyber-Attacks in Power System State Estimation

One major tool of Energy Management Systems for monitoring the status of the power grid is State Estimation. Since the results of state estimation are used within the energy management system, the security of the state estimation process is most important. The focus research in this area is on detecting False Data Injection attacks on measurements. While this is important, State Estimation also rely on database that are used to describe the relationship between measurements and systems' states. This paper presents a two-stage programming framework to detect and correct attacks in the parameters of the measurement model used by the state estimation process in the Energy Management System. In the first stage, an estimate of the line parameters ratios are obtained. In the second stage, the estimated ratios from stage I are used in a Bi-Level model for obtaining a final estimate of the measurements' model parameters. Hence, the presented framework does not only unify the detection and correction in a single optimization run, but also provide a monitoring scheme for the SE database that is typically considered static. In addition, in the two stages, linear programming framework is preserved. For validation, the IEEE 118 bus system is used for implementation. The results of this paper illustrate the effectiveness of the proposed model for detecting attacks in the database used in the state estimation process.

state estimation, two-stage optimization, cyber-ph↗
DOE OSTI2022

Detection of Stealthy False Data Injection Attacks in Unobservable Distribution Networks

In this paper, a composite scheme is proposed for detecting stealthy data manipulation attacks on distribution system which is unobservable with standard least squares based state estimators. This technique has three stages where the process of data imputation, voltage phasor estimation and the bad data detection are carried out in a systematic manner. The proposed approach is then integrated with moving target defense strategies which perturbs the network parameters to reveal stealthy false data injection attacks. The proposed approach is tested is validated on a three-phase, unbalanced 37-node distribution system and its results are presented. It is shown that the proposed approach has the ability to accurately detect the presence of FDI attacks using limited measurements (i.e., the test system is unobservable).

Rajasekaran, James K.↗
DOE OSTI2021

Data-Driven Probabilistic Anomaly Detection for Electricity Market under Cyber Attacks

Information and communication technologies have been widely used in smart grid for efficient operation. However, these technologies are vulnerable to malicious cyber attacks, which may lead to severe reliability and economic issues. Recently, a variety of data-driven anomaly detection approaches have been explored to detect potential cyber attacks in smart grids. In this paper, we researched on the electricity market data aiming to identify anomalies from the locational marginal prices (LMPs) and provide a new indicator for potential cyber attacks in power grids. Specifically, a novel data-driven probabilistic anomaly detection framework is proposed for electricity market, which consists of three major components: long short-term memory (LSTM) based deterministic electricity price forecasting, probabilistic electricity price forecasting and anomaly detection. This framework is tested on a model-based electricity market simulator under two types of cyber attacks, i.e., load redistribution attack (LRA) and price responsive attack (PRA). Numerical results on the simulated LMPs show that the proposed framework is capable of detecting data anomalies over these attacks.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Prediction of Power Measurements Using Adaptive Filters

With the advent of smart grid concept, Internet of Things (IoT) and the deployment of smart meters, the cyberattack threats on power networks have increased due to the use of communication systems that can be accessed by adversaries. Attackers will have the ability to manipulate the outcomes of smart meters which in turn influence the core application of Energy Management System 9EMS): State Estimation (SE). Bad data analytic tools may fail to detect some attacks into measurements. Meanwhile, Machine Learning (ML) solutions have been proposed for detecting False Data Injection (FDI) attacks. However, there is a lack of ML time-series solutions presented in the state-of-the-art that is yet to be complex. In signal processing, time-series solutions do not only consider the signal, but also the statistics of the signal over time. Therefore, in this paper, a machine learning for time-series solutions is presented as an application to model the measurements of the power grid that are used in SE. The presented model takes into account adaptive linear and non-linear filters: Finite Impulse Response (FIR), and Infinite Impulse Response (IIR). The presented models are implemented and performed on the IEEE-118 bus system. The results indicate the advantage of applying those filters over the state-of-the-art machine learning solutions.

Hamad, Khaled↗
DOE OSTI2021

On the Limits of EM Based Detection of Control Logic Injection Attacks In Noisy Environments

The difficulty in applying traditional security mechanisms in Industrial Control System (ICS) environments makes a large portion of these mission-critical assets vulnerable to cyber attacks. Therefore, there is a dire need for the development of novel security mechanisms specifically designed to protect such critical systems. Recently a lot of attention has been given to mechanisms that exploit the EM emanations of devices for defense purposes. Such practices may lead to the development of robust external and non-intrusive anomaly detection systems. Nevertheless, the majority of current work in the area neglects to consider the implications of real-life environments, particularly environmental noise. In this work, we explore the limits of EM-based anomaly detection towards identifying injection attacks in control logic software in noisy environments. Our study conducted upon both synthetically generated and real signals identified that indeed environmental noise might significantly degrade the accuracy of the anomaly detection process. Experiments done upon synthetic data indicated that assuming that signals are captured with high sampling rates, even minor code injections can be detected with above-90% accuracy in noisy environments where SNR is up to -2dB. This is true even if naive detection methods are considered. Moreover, experiments done using a real-life testbed attest that even single-instruction injections can be detected with near-perfect accuracy in relatively clean environments. Finally, noise-elimination techniques can drastically improve the reliability of the detection mechanism even in noisy environments.

97 MATHEMATICS AND COMPUTING↗