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At least 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Hybrid feature-driven learning system for abnormality detection and localization

A cyber-physical system may have a plurality of monitoring nodes each generating a series of current monitoring node values over time representing current operation of the system. A data-driven features extraction computer platform may receive the series of current monitoring node values and generate current data-driven feature vectors based on the series of current monitoring node values. A residual features extraction computer platform may receive the series of current monitoring node values, execute a system model and utilize a stochastic filter to determine current residual values, and generate current residual-driven feature vectors based on the current residual values. An abnormal detection platform may then receive the current data-driven and residual-driven feature vectors and compare the current data-driven and residual-driven feature vectors with at least one decision boundary associated with an abnormal detection model. An abnormal alert signal may then be transmitted when appropriate based on a result of said comparison.

Abbaszadeh, Masoud↗
DOE OSTI2021

Systems and methods for randomized, packet-based power management of conditionally-controlled loads and bi-directional distributed energy storage systems

The present disclosure provides a distributed and anonymous approach to demand response of an electricity system. The approach conceptualizes energy consumption and production of distributed-energy resources (DERs) via discrete energy packets that are coordinated by a cyber computing entity that grants or denies energy packet requests from the DERs. The approach leverages a condition of a DER, which is particularly useful for (1) thermostatically-controlled loads, (2) non-thermostatic conditionally-controlled loads, and (3) bi-directional distributed energy storage systems. In a first aspect of the present approach, each DER independently requests the authority to switch on for a fixed amount of time (i.e., packet duration). The coordinator determines whether to grant or deny each request based electric grid and/or energy or power market conditions. In a second aspect, bi-directional DERs, such as distributed-energy storage systems (DESSs) are further able to request to supply energy to the grid.

Frolik, Jeff↗
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 OSTI2022

CANShield: Signal-based Intrusion Detection for Controller Area Networks

Modern vehicles rely on complex cyber-physical systems made up of hundreds of electronic control units (ECUs) connected through controller area network (CAN) buses. However, the CAN bus attack surface is increasing due to advanced features in automobiles, making it prone to injection attacks. The ordinary injection attacks disrupt the typical timing properties of the CAN data stream, and the rule-based intrusion detection systems (IDS) can easily detect them. However, advanced attackers can inject false data to the signal level, maintaining the regular pattern/frequency of the CAN messages. Such attacks can bypass the rule-based IDS or any anomaly-based IDS built on binary payload data. To make the vehicles robust against such intelligent attacks, we propose CANShield, a signal-based intrusion detection framework for the CAN bus that consists of three modules. A data preprocessing module handles the high-dimensional CAN data stream at the signal level and make them suitable for any machine learning model. A data analyzer module consists of multiple deep autoencoder networks, each analyzing the time series data from a different perspective. Finally, an attack detection module uses an ensemble method to make the final decision. Evaluation results on a standard signal-based dataset show the effectiveness of the CANShield in detecting five advanced attacks.

Shahriar, Md Hasan↗
DOE OSTI2022

Protecting Smart Buildings with STIG

This is a submission for the 2022 Intern Poster Session. The abstract of the poster is: Smart buildings are getting more common, and so are hackers that target them. The physical systems in our businesses and homes are now vulnerable to cyber attacks. Using STIG, an INL program that turns cybersecurity data into graphs, buildings can be better protected.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

OPERATIONAL TECHNOLOGY BEHAVIORAL ANALYTICS (OTBA) – A DATA-CENTRIC APPROACH FOR REDUCING CYBERSECURITY RISK

This paper provides a summary of the methodology, findings, lessons learned, and insights from an investigation into the feasibility of the Operational Technology Behavioral Analytics (OTBA) cybersecurity approach. The concept was evaluated with data from the National Carbon Capture Center (NCCC) – a U.S. Department of Energy (DOE) funded facility that is managed and operated by Southern Company at Alabama Power’s E. C. Gaston generating power plant in Wilsonville, Alabama. Appropriate data sources for the post-combustion carbon capture system were identified. Infrastructure was deployed to monitor, capture and archive data for the system. Critical parameters for each subsystem were identified and analyzed. Machine-learning algorithms were used to establish and characterize normal operations and subsequently identify anomalies. This effort yielded valuable insights and formed the basis of a data-centric strategy for detecting cyber-attacks along with a coordinated response philosophy. A significant takeaway is that the OTBA cybersecurity approach is quite portable; it can be applied to other critical infrastructure beyond fossil power generation.

Black, Clifton↗
DOE OSTI2022

PAS: Privacy Algorithms in Systems

Today we face an explosion of data generation, ranging from health monitoring to national security infrastructure systems. More and more systems are connected to the Internet that collects data at regular time intervals. These systems share data and use machine learning methods for intelligent decisions, which resulted in numerous real-world applications (e.g., autonomous vehicles, recommendation systems, and heart-rate monitoring) that have benefited from it. However, these approaches are prone to identity thief and other privacy related cyber-security attacks. So, how can data privacy be protected efficiently in these scenarios? More dedicated efforts are needed to propose the integration of privacy techniques into existing systems and develop more advanced privacy techniques to address the complex challenges of multi-system connectivity and data fusion. Therefore, we have introduced Privacy Algorithms in Systems (PAS) at CIKM which provides a venue to gather academic researchers and industry researchers/practitioners to present their research in an effort to advance the frontier of this critical direction of privacy algorithms in systems.

Kotevska, Olivera↗
DOE OSTI2022

Cybersecurity Certification Standard for Distributed Energy Resources

Cybersecurity Certification Standard for Inverter Based Resources led by UL, and contributed to by NREL, is a certification standard for devices and their cyber security practices. The standard, which is being developed and led by UL, contains certification and testing processes for the devices to be certified before they are deployed in the field. The importance of the standard is to ensure that all DER devices have all the five pillars of cybersecurity, in which the baseline cybersecurity posture of the DER industry will be elevated.

certification standard↗
DOE OSTI2023

DER Cybersecurity R&D

The National Renewable Energy Laboratory (NREL) conducted more than 30 assessments for utilities across the United States with a cybersecurity assessment tool based on the U.S. Department of Energy (DOE) Cybersecurity Capability Maturity Model (C2M2) and the National Institute of Standards and Technology (NIST) Cybersecurity Framework (CSF) and focused on business process. With funding from the DOE Office of Renewable Energy and Energy Efficiency Federal Energy Management Program, NREL modified the current cyber governance assessment tool to include an assessment process specifically for distributed energy resources (DERs). The Distributed Energy Resources Cybersecurity Framework (DER-CF) was developed to help federal agencies mitigate gaps in their cybersecurity posture for distributed energy systems.

cybersecurity valuation↗
DOE OSTI2022

Synchrophasors-based Master State Awareness Estimator for Cybersecurity in Power Grid: Testbed Implementation & Field Demonstration

The integration of distributed energy resources(DERs) and expansion of complex network in the distribution grid requires an advanced distributed state estimator to monitor the grid health at micro-level. The distribution state estimator will improve the situational awareness and resiliency of distributed power system. This paper proposes a synchrophasors-based master state awareness (MSA) estimator to enhance the cybersecurity in distribution grid by providing a real-time estimation of system operating states to control center operators. In this paper, the proposed MSA estimator utilizes only phasor measurements, bus magnitudes and angles, from phasor measurement units (PMUs),deployed in local substations, to estimate the system states and also detects data integrity attacks, such as load tripping attack that disconnects the load. To validate the proof of concept, we implement the proposed methodology in cyber-physical testbed environment at the Idaho National Laboratory (INL) Electric Grid Security Testbed. Further, to address the “valley of death” and support technology commercialization, field demonstration is also performed at the Critical Infrastructure Test Range Complex(CITRC) at the INL. Our experimental results reveal a promising performance in detecting load tripping attack and providing an accurate situational awareness through an alert visualization dashboard in real-time

42 ENGINEERING↗
DOE OSTI2022

Cybersecurity for Distributed Wind: MIRACL Advisory Board Meeting 2022

This presentation for the MIRACL Industry Advisory Board summarizes the cybersecurity research for distributed wind that was performed during the project. Highlights include an overview of the distributed wind reference architecture, descriptions of the unique needs and challenges for securing distributed wind, the cyber risk management framework that was developed for this project, and key takeaways for various stakeholders.

17 WIND ENERGY↗
DOE OSTI2023

Pillars of Innovation Across Southeast Idaho & the Interstate-15 Corridor

Since 1949, Idaho National Laboratory (INL) has been home to developing civilian and defense nuclear reactor technologies and managing spent nuclear fuel. Today, INL is the nation’s nuclear energy research laboratory, sustaining the safe and efficient operation of existing reactors, powering science in space, and breaking ground on the future fleet of advanced nuclear reactors. INL is only one of many rising technology resources in the region, however. Along the Interstate 15 (I-15) corridor, technology and cybersecurity industries and intellectual assets are rapidly expanding. Creating an innovation hub in this region would unite capabilities to solve current and future challenges in nuclear reactor sustainment and expanded deployment, integrated fuel cycle solutions, integrated energy systems, advanced materials and manufacturing for extreme environments, and secure and resilient cyber-physical systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Variable Resource Resilience: How Systems Experience Increased Resilience from Variable and Hybrid Resources

Variable resources like wind and solar are often seen as detriments to system resilience rather than benefits because they may not be available with the capacities or services required during a high-impact low-frequency (HILF) event, whether that is a physical threat, natural disaster, or cyber attack. However, resilience goals and metrics are inadequate for electric energy delivery systems with inverter-based resources. Examination of this topic reveals that renewable resources are well suited to combat many resilience hazards due to local resource availability. Metrics that demonstrate the resilience value of variable resources are presented and categorized for resource (wind, solar, storage, hybrid) and installation type (bulk utility scale, behind-the-meter, front-of-the-meter, isolated). Distributed and hybrid systems can further enhance resilience benefits my maximizing resource potential for a locality. A case study demonstrating quantitative resilience benefits from wind alone is provided for St. Mary's, AK, which concludes that hundreds of thousands of dollars are saved by the addition of a wind turbine in the face of realistic fuel shortage and extreme winter weather scenarios.

17 WIND ENERGY↗
DOE OSTI2023

Cybersecurity for System Operators

SERC's System Operator Conference is an annual education and sustainment training for electric power system operators across the southeastern United States. In recognition of the growing importance of cybersecurity for energy systems, a block of instruction on cybersecurity concepts was included for this year's conferences. This presentation provides an overview of the need for and general approach used in the DOE CESER-sponsored Cybersecurity for the Operational Technology Environment (CyOTE) program, tailored for electric power system operators. NERC's updated Cyber Intrusion Guide for System Operators is reviewed, explaining how it is an operationalization of some of CyOTE's principles and approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Smart sensor for online situational awareness in power grids

Waveforms in power grids typically reveal a certain pattern with specific features and peculiarities driven by the system operating conditions, internal and external uncertainties, etc. This prompts an observation of different types of waveforms at the measurement points (substations). An innovative next-generation smart sensor technology includes a measurement unit embedded with sophisticated analytics for power grid online surveillance and situational awareness. The smart sensor brings additional levels of smartness into the existing phasor measurement units (PMUs) and intelligent electronic devices (IEDs). It unlocks the full potential of advanced signal processing and machine learning for online power grid monitoring in a distributed paradigm. Within the smart sensor are several interconnected units for signal acquisition, feature extraction, machine learning-based event detection, and a suite of multiple measurement algorithms where the best-fit algorithm is selected in real-time based on the detected operating condition. Embedding such analytics within the sensors and closer to where the data is generated, the distributed intelligence mechanism mitigates the potential risks to communication failures and latencies, as well as malicious cyber threats, which would otherwise compromise the trustworthiness of the end-use applications in distant control centers. The smart sensor achieves a promising classification accuracy on multiple classes of prevailing conditions in the power grid and accordingly improves the measurement quality across the power grid.

Dehghanian, Payman↗
DOE OSTI2023

Nuclear-Integrated Energy Units: Advancing Cybersecurity for Resilient Energy Systems

Rapidly increasing usage of nuclear-integrated energy units has created new challenges in terms of cybersecurity. This paper discusses the potential cyberthreat challenges and cyber risks associated with the widespread adoption of these units, and the role of artificial intelligence (AI) and machine learning (ML) techniques in enhancing the security and resilience of these systems.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Understanding Resilience Valuation for Energy Systems: An Overview of the NREL-NYSERDA Research Collaboration

Energy resilience has become a national priority as Americans experience more power outages attributed to climate change, extreme weather, and cyber events. Energy system owners and operators recognize that they must prepare differently for outages today than they did in the past. Decision makers at all levels face unique challenges and opportunities in effectively and equitably executing a transition to more resilient power systems. The New York State Energy Research and Development Authority (NYSERDA) recently added resilience to its mission statement. The authority engaged with the National Renewable Energy Laboratory (NREL) in a collaborative effort to: 1) understand options for measuring and valuing resilience investment benefits, and 2) inform how resilience could be operationalized across NYSERDA initiatives and program areas.

ENERGY PLANNING, POLICY, AND ECONOMY↗