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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

D2U: Data Driven User Emulation for the Enhancement of Cyber Testing, Training, and Data Set Generation

Whether testing intrusion detection systems, conducting training exercises, or creating data sets to be used by the broader cybersecurity community, realistic user behavior is a critical component of a cyber range. Existing methods either rely on network level data or replay recorded user actions to approximate real users in a network. Our work is the first to produce generative models trained on actual user data (sequences of application usage) collected on endpoints. Once trained to the user's behavioral data, these models can generate novel sequences of actions %that appear to come from the same distribution as the training data. These sequences of actions are then fed to our custom software via configuration files, which replicate those behaviors on end devices. Notably, our models are platform agnostic and could generate behavior data for any emulation software package. In this paper we present our model generation process, software architecture, and an initial evaluation of the fidelity of our models. Our software is currently deployed in a cyber range to help evaluate the efficacy of defensive cyber technologies. We suggest additional ways that the cyber community as a whole can benefit from more realistic user behavior emulation. The data used to train our model, as well as sample configuration files produced by the model, are available at [redacted].

Oesch, T↗
DOE OSTI2023

Experimental Validation of a Command and Control Traffic Detection Model

Network intrusion detection systems (NIDS) are commonly used to detect malware communications, including command-and-control (C2) traffic from botnets. NIDS performance assessments have been studied for decades, but mathematical modeling has rarely been used to explore NIDS performance. This paper details a mathematical model that describes a NIDS performing packet inspection and its detection of malware's C2 traffic. Here, the paper further describes an emulation testbed and a set of cyber experiments that used the testbed to validate the model. These experiments included a commonly used NIDS (Snort) and traffic with contents from a pervasive malware (Emotet). Results are presented for two scenarios: a nominal scenario and a “stressed” scenario in which the NIDS cannot process all incoming packets. Model and experiment results match well, with model estimates mostly falling within 95 % confidence intervals on the experiment means. Model results were produced 70-3000 times faster than the experimental results. Consequently, the model's predictive capability could potentially be used to support decisions about NIDS configuration and effectiveness that require high confidence results, quantification of uncertainty, and exploration of large parameter spaces. Furthermore, the experiments provide an example for how emulation testbeds can be used to validate cyber models that include stochastic variability.

mathematical model↗
DOE OSTI2024

Enhancing Network Anomaly Detection Using Graph Neural Networks

In the world of Internet of Things (IoT) networks, where devices are constantly communicating, keeping them secure from cyber threats is critical. This paper introduces a novel approach to detecting unusual and potentially harmful activities in these networks using graph neural networks (GNNs). We combine two specific types of GNNs-GraphSAGE and graph attention networks (GAT)-to create a model that understands and represents the behaviors and interactions in a network. GraphSAGE creates an embedding of network activities by examining local data interactions, while GAT directs the model's focus to the most critical interactions. By integrating these two methods in a single model that considers different types of interactions (both host and flow nodes), we aim to create a system that accurately represents the current state of a network and can also spot anomalies effectively while reducing false positives and negatives. Our innovative approach has demonstrated promising results, achieving an accuracy of 98% on the UNSW-NB15 dataset, significantly outperforming standalone GraphSAGE and GAT models. This underscores its potential as a robust framework for securing IoT networks against cyber threats and anomalies.

Marfo, William↗
DOE OSTI2023

Sensor and Actuator Attacks on Hierarchical Control Systems with Domain-Aware Operator Theory

Cyber-Physical Systems (CPSs) provide opportunities for cyber attacks to have physical impacts. Advanced Persistent Threats (APTs) are a subclass of cyber threats that act stealthily to avoid detection and enable long-term attacks. Here, we build on our past work in APT modelling to combine deception-based sensor bias attacks and direct actuator manipulations in attacks against a hierarchical control system. That past work used the Koopman operator to develop a data-driven, domain-aware, optimization-based attacker model. Using an expansion of this model, we compute several different attacks, including multiple simultaneous attacks, against a high-fidelity commercial building emulator and compare the impacts of those attacks to each other. One next step of interest is to construct a defender system, built on the same modelling approach, designed to detect and mitigate such attacks.

koopman operator, Cyber-Physical Security, machine↗
NASA NTRS2020

CyberGAN: Generating High-fidelity Cybersecurity Data With Generative Adversarial Networks

Machine learning for cyber defense offers the promise of detecting adversarial activity against the ground data systems managing critical space assets. A fundamental challenge facing machine learning research in cybersecurity is the lack of high-fidelity, shareable datasets for robust evaluation and testing of machine learning-based solutions. High-fidelity, real-world datasets are necessary for reliable benchmarking of nominal system behavior and malicious activity. Unfortunately, such realistic datasets of both nominal and adversarial activity are rarely shared publicly by data owners due to security and privacy concerns. Besides, the available adversarial data is sparse, which makes training models on malicious activity much harder. This situation has impeded and continues to impede the research and successful adoption of machine learning methods for cyber defense. Researchers have dealt with this problem by generating data within a low-fidelity lab environment, using classified and thus unshareable datasets, or downloading low-fidelity public datasets made available by others. We propose an innovative solution to the problem by employing machine learning methods to generate high-fidelity data. Specifically, we propose the use of Generative Adversarial Networks (GANs) to generate high-fidelity data for cybersecurity purposes. GANs have found successful image processing and natural language applications, but have not yet been investigated for cyber data generation. Our proposed approach first involves training the `discriminator' network of the GAN with a sample of real-world data consisting of malicious and nominal samples. We then use the `generator' network to generate new high-fidelity data samples consisting of an appropriate mix of malicious and nominal activity. We demonstrate applications of our architecture by generating high-fidelity cybersecurity data containing both malicious and nominal samples. We thoroughly evaluate the fidelity of our generated data using heuristics and evaluate its usefulness for machine learning applications using three different datasets. Overall, our approach results in high-fidelity, shareable datasets.

Zhang, Yuening↗
DOE OSTI2021

An Active Detection Scheme for Sensor Spoofing in Grid-tied PV Systems

In this paper an active detection scheme for sensor spoofing (manipulated externally via a cyber attack) in grid-tied PV systems is discussed. The core of the proposed active detection scheme is to introduce a private (secret) watermarking signal into the control inputs of the DC-DC converter and DC-AC inverter stages to detect any malicious spoofing (manipulation) of voltage/current sensor measurements controlling both the DC-DC converter maximum power point tracking (MPPT) stage and the DC-AC inverter of the grid-tied PV system. Several types of possible spoofing mechanisms (attack models) are discussed. The proposed sensor spoofing attack detector system consists of injecting a small magnitude of digital watermarking signal (DWS) and conduct three statistical watermark tests on the reported sensor measurements to determine if a) the proposed system is healthy and operating as expected b) if sensor signals were spoofed (manipulated) externally or c) if a particular sensor is malfunctioning due to a faulty hardware. It is shown via extensive simulations that the proposed DWS approach is robust in detecting malicious external manipulation of sensors controlling the grid tied PV system. A testing platform is currently under development and the experimental results will be discussed in the conference presentation.

Ibrahim, Hasan↗
DOE OSTI2021

Synchrophasor spoofing detection and remediation for wide-area damping control

Evolving cyber-attack threats put at risk automatic closed-loop systems to be incorporated in the smart grid. Wide-area control systems are particularly vulnerable to signal spoofing attacks due to sensor remoteness and dependence on satellite communication for time synchronization. A successful cyber-attack on a wide-area controller has the potential to reduce relative stability of the power system or worse, destabilize it. As such, detection algorithms must be deployed as defense against such attacks with the ability to autonomously correct for detected tampering or misoperation. The Spoof Catch and Restore Routine (SCR 2 ), a combination of three real-time spoof detectors, each requiring limited information about the plant, is reported here. Nonlinear simulations of a compromised wide-area control system deployed in the Western Interconnection show the effectiveness of SCR 2 in detecting both delay-type and counterfeit-type spoofing attacks on wide-area sensors.

42 ENGINEERING↗
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

WISP: Watching grid Infrastructure Stealthily through Proxies (Final Technical Report)

The complex interdependencies of cyber systems (sensors and communications), physical grids and associated electricity market operations make protecting electric power grids a significant challenge. The energy sector is constantly under new, targeted, advanced and dangerous cyber-attacks that have the potential to result in the loss of human life. These threats are further exacerbated by our need to modernize the grid. One focus of cyber security research in smart grids is the securing of the SCADA system through advanced intrusion detection systems (IDS) and bad data detection algorithms in state estimation. These methods either require full knowledge of the system topology and parameters or fail to understand the physical behaviors under attack. WISP (Watching grid Infrastructure Stealthily through Proxies) is designed to provide additional protection to the power grid using only publicly available data. In particular, WISP exploits the spatio-temporal nature of the real time locational marginal prices (LMPs), in conjunction with other information such as bids, weather, outages and load data to analyze anomalous power pricing behaviors and then correlate those observations to localize regions of interest and identify potential cyber events. WISP is non-intrusive as the tool is deployed as a service in the Cloud or on premise and provides reliable information to system operators for enhanced situational awareness, without impeding energy delivery functions. The WISP technology comprises three modules: the data-driven anomaly detection core, the vulnerability and risk analysis and the root cause analysis. The data-driven anomaly detection core performs the tasks of feature selection, anomaly detection and attack region localization. The vulnerability and risk analysis module provides system level information of the vulnerable variables and times, assisting the operators in selecting monitoring and protection nodes. The root cause analysis module takes the detection results and identifies potential operational conditions that contribute to the detected anomalies. In Phase I, we have demonstrated the feasibility and effectiveness of WISP. We developed a realistic electricity market simulator capable of generating normal and attack market data under various operational conditions. We developed a series of cyber-attack detection and analysis algorithms and evaluated them under multiple data sources. Finally, we integrated all modules into an end-to-end software, providing functions for data management, data analytics and visualization. Specifically, we have achieved: (i) real-time data acceptance from external utility interfaces with >99% acceptance rate; (ii) high performance anomaly detection algorithms with >98% detection accuracy and <0.1% false alarm rate; and (iii) ultra-low computing delay <50 milliseconds. Additionally, our team developed algorithms to identify the vulnerable variables in electricity market operations and root cause analysis functions to identify major contributors to the price spikes. These ancillary modules are necessary when deploying WISP in real world industry environment. In Phase II, we have demonstrated the effectiveness of WISP software on realistic largescale power systems. We performed red team testing for the Phase I WISP software and identified software vulnerabilities and implemented corresponding mitigation solutions. We adapted the electricity market simulator for the Texas synthetic 2000-bus system and generated datasets for the false data injection attacks. We created database and visualization interfaces for the Texas system and the ISO New England system. We performed software optimization in terms of operation efficiency, computing speed and detection accuracy. Finally, we tested the software on the Texas system and the ISO New England system and evaluated the detection performance. Overall, we achieved above 89% detection rate, below 3% false alarm rate and below 37 seconds of end-to-end detection delay.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Cybersecurity Framework Tool

The Cybersecurity Framework Tool is a web-based tool that provides five (5) concurrent and continuous functions to identify, detect, protect, respond and recover from cyber security threats to critical infrastructure.

Mylrea, Michael↗
DOE OSTI2022

Equipment Testing Environment (ETE) Specification

Cyber security has been difficult to quantify from the perspective of defenders. The effort to develop a cyber-attack with some ability, function, or consequence has not been rigorously investigated in Operational Technologies. This specification defines a testing structure that allows conformal and repeatable cyber testing on equipment. The purpose of the ETE is to provide data necessary to analyze and reconstruct cyber-attack timelines, effects, and observables for training and development of Cyber Security Operation Centers. Standardizing the manner in which cyber security on equipment is investigated will allow a greater understanding of the progression of cyber attacks and potential mitigation and detection strategies in a scientifically rigorous fashion.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2021

Simulation and Modeling Concepts for Secure Airspace Operations

This paper examines cyber security vulnerabilities of Urban Air Mobility operations. With the expected advent of new entrants including Unmanned Aerial Systems, Commercial Launch Vehicles and Urban Air Mobility aircraft, the future United States National Airspace System will have to evolve to include their operations along with the current commercial, general aviation and military operations. The National Aeronautics and Space Administration and the Federal Aviation Administration are working together to provide a vision for aviation operations in the future—2045 and beyond. Their National Airspace System Horizons initiative seeks to provide stakeholders a list of operational scenarios and technologies, concepts and strategies needed for supporting that vision. They have identified cybersecurity as one of the seven strategic interest areas for realizing this vision. Consequently, NASA is studying cyber resiliency for secure airspace operations. While there are many pathways to attack a cyber physical system such as Urban Air Mobility, their effect is expressed in modification or corruption of data/information used for controlling vehicles and making operational decisions. The paper describes cybersecurity technologies of Encryption, Blockchain, Virtual Information Fabric Infrastructure, Trusted Platform Module and Anomaly Detection for protecting the data, and the cyber resiliency of the current and future air traffic management system.

Urban Air Mobility↗
NASA NTRS2021

Simulation and Modeling Concepts for Secure Airspace Operations

This paper examines cyber security vulnerabilities of Urban Air Mobility operations. With the expected advent of new entrants including Unmanned Aerial Systems, Commercial Launch Vehicles and Urban Air Mobility aircraft, the future United States National Airspace System will have to evolve to include their operations along with the current commercial, general aviation and military operations. The National Aeronautics and Space Administration and the Federal Aviation Administration are working together to provide a vision for aviation operations in the future—2045 and beyond. Their National Airspace System Horizons initiative seeks to provide stakeholders a list of operational scenarios and technologies, concepts and strategies needed for supporting that vision. They have identified cybersecurity as one of the seven strategic interest areas for realizing this vision. Consequently, NASA is studying cyber resiliency for secure airspace operations. While there are many pathways to attack a cyber physical system such as Urban Air Mobility, their effect is expressed in modification or corruption of data/information used for controlling vehicles and making operational decisions. The paper describes cybersecurity technologies of Encryption, Blockchain, Virtual Information Fabric Infrastructure, Trusted Platform Module and Anomaly Detection for protecting the data, and the cyber resiliency of the current and future air traffic management system.

Urban Air Mobility↗
DOE OSTI2025

Digital Assurance Checklist for Homeowners and Installers

This document provides a comprehensive Digital Assurance Checklist for securing behind-the-meter energy assets, focusing on both installers and homeowners. As distributed energy resources (DERs) such as solar PV and battery storage become integral to residential energy systems, cybersecurity emerges as a critical component of reliability and safety. The guide outlines actionable steps for installers during pre-installation, commissioning, and post-installation phases, emphasizing practices like network segmentation, credential management, firmware validation, and homeowner education. For homeowners, the document introduces a tiered approach to cyber hygiene—from essential measures like strong Wi-Fi credentials and automatic updates to advanced strategies such as network segmentation, DNS filtering, and intrusion detection. By adopting these practices, stakeholders can mitigate cyber risks, safeguard energy infrastructure, and ensure resilient, secure operation of DER systems. Additional resources and references to industry standards are included to support implementation.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Digital Assurance Checklist for Homeowners and Installers

This document provides a comprehensive Digital Assurance Checklist for securing behind-the-meter energy assets, focusing on both installers and homeowners. As distributed energy resources (DERs) such as solar PV and battery storage become integral to residential energy systems, cybersecurity emerges as a critical component of reliability and safety. The guide outlines actionable steps for installers during pre-installation, commissioning, and post-installation phases, emphasizing practices like network segmentation, credential management, firmware validation, and homeowner education. For homeowners, the document introduces a tiered approach to cyber hygiene—from essential measures like strong Wi-Fi credentials and automatic updates to advanced strategies such as network segmentation, DNS filtering, and intrusion detection. By adopting these practices, stakeholders can mitigate cyber risks, safeguard energy infrastructure, and ensure resilient, secure operation of DER systems. Additional resources and references to industry standards are included to support implementation.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

EV SALaD 2023 Demonstration: Best Practices and Mitigations for Protecting EVSE Infrastructure

The Electric Vehicle Secure Architecture Laboratory Demonstration (EV SALaD) program is a demonstration of cybersecurity best practices for high-power electric vehicle (EV) charging infrastructure led by Idaho National Laboratory (INL), in collaboration with other DOE National Laboratories participating in the EVs at Scale Consortium.a Sandia National Laboratories (SNL) and Pacific Northwest National Laboratory (PNNL) participated in the first 2-year (FY22-23) demonstration cycle for EV SALaD. This report documents the FY23 demonstration, the second in a series of demonstrations and collaborations in deploying and operating cybersecure EV charging infrastructure. It includes a summary of improvements from the FY22 demonstration, technical analysis of the FY23 demonstration, how the research demonstrates cyber-physical and cybersecurity best practices for high-power EV charging infrastructure, and related impacts to national and energy security. For EV SALaD, the FY22 demonstration focused on the detection, ranking, and prioritization of anomalous events for high-power EV charging. The FY23 demonstration additionally included the demonstration of cybersecurity best practices, which included protection and mitigation solutions to prevent, respond, and recover from anomalous events. During the demonstrations, the multi-lab EV SALaD team conducted a Test Effect Payload (TEP)b evaluation on extreme fast charger (XFC) hardware equipped with Cerberus, a detection and response solution, to demonstrate anomaly detection and mitigation cybersecurity best practices against cyber-enabled events.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Securing Inverter Communication: Proactive Intrusion Detection and Mitigation System to Tap, Analyze, and Act

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.

14 SOLAR ENERGY↗