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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

HIDES: Hybrid Intrusion Detector for Energy Systems

The establishment of a resilient electric grid accompanied by a secure communications network is an ongoing battle as advanced persistent threats continue to exploit existing vulnerabilities in legacy supervisory control and data acquisition system (SCADA) infrastructure. Traditional intrusion detection systems (IDSs) lack consistent performance because of the continuously evolving attack surface of SCADA systems. These shortcomings can be overcome by integrating logical system behavior, protocol-specific knowledge, and data-based learning to develop a comprehensive IDS solution. In this paper, we present a Hybrid Intrusion Detector for Energy Systems by integrating a network-based IDS, state-of-the-art machine learning-based IDS, and model-based IDS to detect unknown and stealthy cyberattacks targeting the SCADA networks. The proposed IDS uses synchrophasor measurements and cyber logs to learn patterns of different scenarios based on spatiotemporal behaviors of power systems. As a proof of concept, we implement and validate the proposed IDS by leveraging resources available at the National Renewable Energy Laboratory's Energy Systems Integration Facility test bed. Experimental results show promising performance in detecting cyberattacks while providing realtime visualization of power system measurements and cyber logs.

machine-learning intrusion detection system↗
DOE OSTI2022

Machine Learning-based False Data Injection Attack Detection and Localization in Power Grids

Cyberattacks on critical infrastructures can be catastrophic and bring nations to their knees. Therefore, detecting these attacks is crucial and challenging. This paper presents a novel approach for detecting and locating cyberattacks affecting an electrical power system. The adversary employs a man-in-the-middle technique to inject false data into the communication between distributed energy resources (DER) and Microgrid Controller (MGC) with the goal of disrupting power delivery. The approach for detection and localization is based on integrating multiple machine learning-based anomaly detection models that combine network traffic data and grid measurements. Experiments are performed to assess the method's performance using a hardware-in-the-loop real-time simulation testbed which includes Modbus TCP/IP communication. Power system topology and operating conditions are based on actual topology and real-world data provided by the Holy Cross Energy utility network. Results confirm that the method can be successfully employed for detecting and localizing cyberattacks.

Leao, Bruno P.↗
DOE OSTI2026

The Impact of Time-Aware Design Choices in ICS Anomaly Detection

Industrial control systems (ICS) remain vulnerable to increasingly sophisticated cyberattacks, yet evaluating anomaly detection models in these environments is challenging due to temporal dependencies, missing-not-at-random patterns, and extremely imbalanced datasets. These factors make common practices—especially random data splits and na¨ıve imputation— prone to severe temporal leakage, which can inflate reported performance and obscure real-world limitations. In this work, we systematically examine classical machine learning models, temporal deep learning architecture, and tensordecomposition– based methods on a gas-pipeline dataset using a fully temporally separated evaluation pipeline designed to mimic realistic deployment conditions. Our findings show that proper temporal handling and MNAR-aware preprocessing significantly alter the relative performance of popular anomaly-detection methods, providing practical guidance for designing reliable, leakage-resistant ICS intrusion-detection systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

On Anomaly Detection for Transactive Energy Systems with Competitive Market

Two anomaly-detection criteria are proposed for transactive energy systems with competitive markets. Participants of transactive energy systems seek an optimal power allocation through hybrid economic-control methods to facilitate the integration of various types of distributed energy resources to power distribution systems. In transactive energy systems, every participant is assumed to be a rational entity, and consumers have diminishing marginal utility and suppliers have increasing marginal cost. With the first proposed anomaly-detection criterion, the monotonicity of marginal cost and marginal utility are examined. The impact of line flow constraints is also taken into consideration. Then, the second anomaly-detection criterion is proposed for TESs with marginal cost and marginal utility which change faster than a certain rate. The second criterion is more accurate than the first one for TES with marginal cost and marginal utility which change faster than a certain rate, but it requires the knowledge of that rate. Neither criteria requires more data than those necessary to find the optimal power allocation and the market-clear price in a transactive energy system. Therefore, the proposed criteria do not disclose any more data than necessary. As the monotonicity of marginal cost and marginal utility in a TES with competitive markets results in convex objective functions in an optimization problem and strongly convex ones when marginal cost and marginal utility changes faster than a certain rate, the two detection criteria are also applicable to anomaly detection of general convex optimization problems. Simulations are carried out to show the efficacy of the proposed criteria to detect anomalies caused by cyberattacks.

Wang, Peng↗
DOE OSTI2025

Design Choices in Anomaly Detection for Industrial Control Systems: Insights from Gas Pipeline Data

Industrial control systems (ICS) remain vulnerable to increasingly sophisticated cyberattacks, yet evaluating anomaly detection models in these environments is challenging due to temporal dependencies, missing-not-at-random patterns, and extremely imbalanced datasets. These factors make common practices—especially random data splits and naïve imputation—prone to severe temporal leakage, which can inflate reported performance and obscure real-world limitations. In this work, we systematically examine classical machine learning models, temporal deep learning architecture, and tensor-decomposition–based methods on a gas-pipeline dataset using a fully temporally separated evaluation pipeline designed to mimic realistic deployment conditions. Our findings show that proper temporal handling and MNAR-aware preprocessing significantly alter the relative performance of popular anomaly-detection methods, providing practical guidance for designing reliable, leakage-resistant ICS intrusion-detection systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Resilient Energy Delivery and Control Systems (REDCS) (Final Technical Report)

US critical infrastructure is increasingly the target of cyberattacks, where disturbances could cause considerable damage and disruption. To help provide a new layer of cyber-physical protection for one key energy delivery system, natural gas pipelines, GE Vernova Advanced Research along with partners Florida State University and Intel Corporation created an innovative technology called "Resilient Energy Delivery and Control Systems" (REDCS). This cybersecurity package helps detect anomalies caused by cyberattacks, isolate the subsystem being impacted by the attack, and provide functions that can allow for resiliency – giving better situational awareness to the operators and cybersecurity specialists or in the future perform closed loop control for continued operation while compromised.

03 NATURAL GAS↗
DOE OSTI2025

Adaptive anomaly detection for identifying attacks in cyber-physical systems: A systematic literature review

Modern cyberattacks in cyber-physical systems (CPS) rapidly evolve and cannot be deterred effectively with most current methods, which focus on characterizing past threats. Adaptive anomaly detection (AAD) is among the most promising techniques to detect evolving cyberattacks, with an emphasis on fast data processing and model adaptation. AAD has been researched extensively; however, to the best of our knowledge, our work is the first systematic literature review (SLR) on current research in this field. We present a comprehensive SLR, gathering 397 relevant papers and systematically analyzing 65 of them (47 research and 18 survey papers) on AAD in CPS from 2013 to November 2023. We introduce a novel taxonomy considering attack types, CPS application, learning paradigm, data management, and algorithms. Our findings show that most studies addressed either model adaptation or data processing, but rarely both simultaneously. This indicates a research gap in fully adaptive solutions. We also categorize algorithms, datasets, and attack characteristics, and summarize strengths and weaknesses across the literature. Our review provides a structured and accessible reference for researchers and practitioners, offering insights into key trends and highlighting limitations in current approaches. Finally, we outline several future research directions, including the need for integrated real-time processing and adaptive learning, explainability, and uncertainty quantification in AAD for CPS.

Adaptation↗
DOE OSTI2023

Machine Learning-based Intrusion Detection for Smart Grid Computing: A Survey

Machine learning (ML)-based intrusion detection system (IDS) approaches have been significantly applied and advanced the state-of-the-art system security and defense mechanisms. In smart grid computing environments, security threats have been significantly increased as shared networks are commonly used, along with the associated vulnerabilities. However, compared to other network environments, ML-based IDS research in a smart grid is relatively unexplored, although the smart grid environment is facing serious security threats due to its unique environmental vulnerabilities. In this article, we conducted an extensive survey on ML-based IDS in smart grids based on the following key aspects: (1) The applications of the ML-based IDS in transmission and distribution side power components of a smart power grid by addressing its security vulnerabilities; (2) dataset generation process and its usage in applying ML-based IDSs in the smart grid; (3) a wide range of ML-based IDSs used by the surveyed papers in the smart grid environment; (4) metrics, complexity analysis, and evaluation testbeds of the IDSs applied in the smart grid; and (5) lessons learned, insights, and future research directions.

SCADA↗
DOE OSTI2026

Towards Secure Autonomous Vehicles: An Integrated Edge and Multi-Modal Machine Learning Framework for Intrusion Detection

Autonomous vehicles (AVs) are vulnerable to cyberattacks targeting both internal communication networks and external perception sensors. While edge-based intrusion de- tection for Controller Area Network (CAN) buses offers real-time protection, it cannot detect cross-modal threats. Conversely, multi-modal fusion approaches improve coverage but often lack efficiency for in-vehicle deployment. This thesis integrates two complemen- tary solutions: (1) a lightweight, edge-deployable machine learning framework for CAN bus intrusion detection, and (2) a late-fusion system combining CAN FD and LiDAR data. Together, they form a hierarchical defense capable of handling single-modality and coordi- nated attacks. Simulations show that CAN-only models reach 93% accuracy on simulated DoS, spoofing, replay, and fuzzy attacks, while the fusion system achieves 0.87 AUC and 0.82 F1-score at 2 ms latency. This unified framework establishes a scalable, explainable, and field-ready strategy for AV cybersecurity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

DER Cybersecurity Detection and Response Suite

SAND2024-08475O The Distributed Energy Resource (DER) Cybersecurity Detection and Response Suite is a solution for distributed energy resource (DER) systems. The DER Security Orchestration, Automation, and Response (SOAR) solution that uses alerts from signature- and behavior-based Intrusion Detection Systems are intended to be deployed as bump-in-the-wire (BITW) devices in front of DER equipment. The fielded application would use multiple intrusion detection systems that report data to SOAR to respond to cyberattacks. The suite consists of two software components: • The proactive intrusion detection and mitigation system (PIDMS) secures grid-edge photovoltaic smart inverters and other equipment in distributed energy resource systems. It is a distributed BITW solution; cyber and physical data are automatically processed using network inspection tools and custom machine learning algorithms to detect abnormal events and correlate cyber-physical events. • The Security Orchestration, Automation, and Response for Distributed Energy Resources (SOAR4DER) application ingests data from several intrusion detection systems to quickly block attacks and revert DER systems to good states. Using a collection of intrusion detection system technologies on a BITW device, it incorporates physical and cyber data to detect abnormal and potential malicious behaviors. Multiple SOAR playbooks then use the intrusion detection system data streams to automatically defend the system. SOAR4DER system testing showed detection and response times under 30 seconds for all adversary reconnaissance, denial-of-service attacks, malicious Modbus commands, brute-force logins, and machine-in-the-middle attacks. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Johnson, Jay↗
DOE OSTI2026

Cybersecurity Center for Offshore Wind Energy (Final Project Report)

This project establishes a Cybersecurity Center for Offshore Wind Energy with the objective of designing and operating a cyber-physical testbed for wind energy farms (WEFs) that enables comprehensive cybersecurity research. The testbed incorporates a Supervisory Control and Data Acquisition (SCADA) system connected to turbine models via industrial-grade programmable logic controllers (PLCs) and remote terminal units (RTUs). It supports side-channel data acquisition, implementation and analysis of various cyberattack scenarios, and development of attack detection, mitigation, and best-practice guidance tailored to wind energy systems. During the project, the team expanded the number and fidelity of mathematical turbine models (MTMs), integrated these models with SCADA infrastructure, and deployed a scaled physical turbine and associated sensors. High-resolution operational and side-channel data streams were collected and used to refine machine-learning (ML)-based attack detection systems and to extend the WindCRAFT framework to multi-turbine threat scenarios. The project demonstrated a realistic, scalable environment for evaluating cyber threats, validated attack detection approaches using enriched datasets, and identified new multi-turbine and inter-turbine communication attack vectors. The resulting testbed, models, and security mechanisms provide a foundation for ongoing R&D and deployment of cyber-resilient offshore wind energy systems.

17 WIND ENERGY↗
DOE OSTI2024

Data Security Defense: Modeling and Detection of Synchrophasor Data Spoofing Attack for Grid Edge

Data security and cyberattack have become critical issues in the distributed power system where adversaries can swap the source information of sensors or even spoof and alter measurements. However, the cyber security of the power system is challenged by the unpredictability and stealth of the spoofing attacks. Here, to protect the data security at the grid edge, this paper developed a synchrophasor data spoofing attack detection framework based on the time-frequency feature extraction techniques including the short-time Fourier transform (STFT) and object detection network for real-time synchrophasor data categorization and spoofing attack localization. The proposed approach outperforms earlier work in terms of spoofing attack detection and offers a vital localization function employing distributed synchrophasor sensors.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Cyber-Physical Resiliency for Wind Power Generation

During the next three years, wind power generation is expected to add more generation capacity to the national electrical grid than any other energy sector. With new wind turbine designs rated power over 10 MW and wind farms reaching over 1 GW capacities, the consequences of cyber-attacks on wind power generation are becoming increasingly more critical. Moreover, the known vulnerabilities of wind turbine control systems and the potential damaging effects of intrusions, motivate an urgent protection improvement for the wind power generation sector. This program has developed a variety of new adaptive defense technologies that enable wind power generation systems to survive sophisticated cyberattacks by enhancing the control systems capabilities of detection, localization, and accommodation. The introduction of these technologies in the on-shore and the emerging off-shore market will result in a significantly more reliable and secure wind power infrastructure.

17 WIND ENERGY↗
DOE OSTI2026

Feature Engineering and Ensemble Methods for Imbalanced ICS Intrusion Detection: Pipeline Audit and Constrained Evaluation

Industries are becoming increasingly connected and are more vulnerable to cyberattacks due to the widened attack surface. Industrial Control Systems (ICS) are among the most critical sectors that malicious actors can target, as such attacks can cause significant operational disruption and physical damage. It is imperative to detect such attacks as early as possible. This paper evaluates constraint-conditioned optimistic performance estimates for traditional ML models in ICS intrusion detection (i.e., estimates obtained under contiguous, non-shuffled temporal evaluation without test-set alteration, but with pre-split feature engineering that may introduce temporal leakage, due to dataset constraints). Our findings are threefold. First, we quantify how iterative feature engineering affects tree-based ensemble performance and examine how pipeline decisions (split strategy, sampling scope, and cleaning policy) can inflate or reduce reported IDS results under constraint-bound evaluation. Second, we compare intrinsic class-imbalance handling across ensemble models. Third, under our current pipeline constraints (including pre-split feature engineering), CatBoost achieves the best performance on Water Storage Tank (accuracy: 0.9831, class-1 F1: 0.9682), while Light- GBM achieves the best performance on Gas Pipeline (accuracy: 0.9618, class-1 F1: 0.9086).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Cybersecurity Challenges in Low-Inertia Power-Electronics-Dominated Grids

Here, the low inertia characteristics of the power electronics dominated grid (PEDG) introduces challenges while restoring voltage and frequency to their nominal values. These stability challenges create new cybersecurity vulnerabilities that are not thoroughly discussed in the literature. Cyber events such as false data injection (FDI), denial of service (DoS), man-in-the-middle attacks, stealthy attacks, and advanced persistent threats target PEDG to disrupt grid stability or gain financial benefits. The low inertia of PEDG (< 2s) compared to traditional grids (~10s) exacerbates these vulnerabilities. In response to stealthy attacks on state variables that supervisory layers cannot detect until significant harm occurs, the low inertia characteristics of PEDG offer substantial stealthy attack surfaces. To counteract such threats, PEDG must be equipped with ultra-fast real-time anomaly detection system and trajectory prediction mechanism to achieve effective cyberattack resiliency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Model-Based Detection of Coordinated Attacks (DCA) in Distribution Systems

The fast-paced growth in digitization of smart grid components enhances system observability and remote-control capabilities through efficient communication. However, enhanced connectivity results in heightened system vulnerability towards cybersecurity risks in the cyber-physical power system. Coordinated cyber-attacks (CCA), when undetected, lead to system-wide impact in terms of large disturbances or widespread outages. Detecting CCA in the cyber layer is critical to thwart cyber-attacks in real-time before the attack impacts the physical system. The challenge of locating CCA stems from the complex grid dynamics, making it difficult to distinguish between normal operational variations and cyber-attack impact. CCA often employs multiple attack vectors targeting geographically distributed components, further complicating CCA identification. Existing research in intrusion detection is primarily focused on the transmission network and limited to detecting individual attacks. In this paper, a novel proactive DCA strategy is proposed for early detection of CCA by establishing correlations among distinct attack events through model-based reinforcement learning that utilizes abductive reasoning to conclude the attacker goal. The solution includes understanding the system model, learning the system dynamics, and correlating individual cyber-attacks to extract the attacker’s objective. The developed learning algorithm identifies the most probable attack path to reach the attacker’s objective by predicting the next attack steps. A DNP3-based cyber-physical co-simulation testbed is developed to test the proposed algorithm using the IEEE 13-node test feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Using Structured Intelligence Graph (STIG) to protect our critical infrastructure against cyber attacks [Poster]

STIG is a revolutionary cybersecurity tool developed by researchers at the U. S. Department of Energy's Idaho National Laboratory and it is a software that allows utility owners and operators to easily visualize, create, and edit cyberthreat intelligence information. STIG uses Structured Threat Information eXpression (STIX) and converts complex data on cybersecurity vulnerabilities into a visualization that is easy to understand and act on. With STIG, utility owners and operators have a common system for sharing threat intelligence information, thus increasing the chances of detecting and mitigating cyber exploits before they lead to a cyberattack.

24 POWER TRANSMISSION AND DISTRIBUTION↗