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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Cyber-Attack Identification of Synchrophasor Data Via VMD and Multifusion SVM

A large amount of synchrophasor data in the wide area measurement system (WAMS) needs to be collected and transmitted to the phasor data concentrator, thereby increasing the possibility of being attacked by hackers. The attacked data are therefore hidden into the normal synchrophasor data so that the synchrophasor data based application will be affected. To remedy this problem, an identification framework is proposed to detect the data cyber-attack in WAMS utilizing variational mode decomposition (VMD) and multifusion support vector machine (MSVM). First, VMD is used to transform the attacked data into multiple modal components. Thereafter, a novel MSVM is employed to classify the deterministic features using the proposed linear combined multikernel (LCM). Further, this LCM can fuse multiple types of features, including the time, frequency, and statistical domains of the synchrophasor data. Utilizing the actual data from FNET/GridEye, different experiments are conducted under multiple attack strengths and types. The results demonstrate that the identification framework has higher precision and robustness compared with other conventional classifiers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Robust Restoration From Cyber-Physical Attacks in Active Distribution Grids With Grid-Edge IBRs

The inverter-based resources (IBRs) have enabled the integration of renewable energy at the grid edge with enhanced control capabilities to support the reliable operation of power grids. Different control frameworks, such as hierarchical or distributed architecture, have been proposed with the expansion of cyber networks for real-time monitoring and control. This evolution of critical infrastructure into cyber-physical systems also brings more vulnerabilities for the broadened attack surfaces, and significantly increases the possibility of physical system failures or outages caused by cyberattacks. Among tremendous efforts in the defense-in-depth approach, it remains challenging to provide prompt detection and accurate location of attack entry points or paths. Therefore, the prevailing restoration framework may struggle to fully consider the cyber-physical interdependence, successfully isolate the compromised cyber and physical components, and safely recover the systems without the potential risks leading to secondary outages. This paper is motivated to develop a cyber-physical restoration framework for distribution grids to recover from cyber attacks by harnessing grid-edge IBRs. The framework is first built on the operational guidelines of IBRs considering the compromised cyber layer. Then, an ambiguity set is established to represent the uncertainty of attack scenarios and their possibility levels. Next, a distributionally robust optimization model is developed to provide the optimal load restoration strategy across all scenarios. The effectiveness of the proposed model is demonstrated through various use cases on the modified IEEE 13-node and 123-node test systems. Finally, simulation results demonstrate the effectiveness and advancement of developed post-attack restoration strategies.

Cybersecurity↗
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 OSTI2022

Security Enhancement of Network Constraint Grid-Edge Energy Management System

Network constrained grid edge energy management system (EMS) provides economic solution for active and reactive power dispatch of distributed energy resources (DERs) at the grid edge level. Grid edge EMS ensures secure interconnection of a circuit segment to the distribution system by maintaining grid code requirements (e.g. IEEE 1547–2018). Grid edge EMS is dependent on communication to receive load measurement, which brings a risk of unobservable false data injection attacks (FDIAs). To mitigate the risk, this paper proposes a framework to enhance resilient operation of grid edge EMS by detecting the unobservable FDIAs on loads and replacing them with forecasted values. In this work, a two-step detection algorithm is proposed. In first step, conventional residual based algorithm is deployed. Autoencoder (AE) based data driven mechanism is included in second step to detect the presence of unobservable FDIAs. After ensuring the presence of FDIA, its specific location is detected by checking the maximum residue values till the predefined threshold value is reached. Detected false data injected loads are then replaced with forecasted load values following long-short term memory (LSTM) based forecast to ensure resilient performance of grid edge EMS in the presence of attacks. This proposed security enhancement framework for grid edge EMS is evaluated in IEEE 13 bus system with three integrated DERs. Numerical simulation shows the validation of the proposed framework by reducing voltage violation in real operation of grid edge EMS.

cyber attack detection↗
DOE OSTI2021

Programmable intrusion detection for distributed energy resources in cyber–physical networked microgrids

We present a programmable intrusion detection method is presented to identify the malicious attacks to distributed energy resources (DERs) in the cyber-physical networked microgrids. The proposed method injects small programmable signals into the system and uses the response to identify abnormal conditions. Because of the low or even zero inertia induced by integrations of DER power-electronic-interfaces, microgrids have very limited resilience capability; and thus, being sensitive to attacks. One microgrid's malfunction caused by attacks can easily propagate to its neighboring systems when several microgrids are connected, leading to catastrophic electricity supply failures. Through the presented method, malicious intrusions can be effectively detected, located, and defended for securing microgrids. Theoretical derivations are provided to define the programmable detection rules. The detection rule is easy and flexible to update, making it difficult for attack actors to gain the knowledge of the detection rules, in order to avoid being detected. Numerical results on a cyber-physical networked microgrids system show that the proposed method is effective and efficient in precisely locating intrusion attacks to the microgrids system.

42 ENGINEERING↗
DOE OSTI2020

Data-based and secure switched cyber–physical systems

In this work, we develop a completely model-free moving target defense framework for the detection and mitigation of sensor and/or actuator attacks in cyber–physical systems with dynamics that evolve in discrete-time. We incorporate an intrusion detection mechanism based on an approximate dynamic programming technique that learns the policies for optimal regulation and optimal tracking while simultaneously defending against actuator and sensor attacks in a model-free fashion. Switching rules are leveraged to force proactive and reactive defense mechanisms as well as, guarantee the stability of the equilibrium point. Finally, as a case study, we apply the proposed moving target defense framework to a DC–DC converter that is used in electric vehicles.

42 ENGINEERING↗
DOE OSTI2021

Frequency Injection Based HVDC Attack-Defense Control Via Squeeze-Excitation Double CNN

Due to the independent controllability and fast power regulation capability, the High Voltage Direct Current (HVDC) system could be a prospective technology to provide multiple ancillary services to the system besides conventional bulk power transmission. However, with the increase of False Data Injection Attacks (FDIAs) on PMU data, the HVDC system could have the wrong response once the collected data that the HVDC system relied on is attacked, thus threatening the system operating security. How to ensure the security of the PMU-based HVDC ancillary service control become an urgent issue. To mitigate the risk, this paper proposed an HVDC attack-defense control based on the FDIAs detection method. Firstly, the Squeeze-Excitation based Double Convolutional Neural Networks (SE-DCNN) is proposed to realize fast identification of the attacking frequency type based on the time and frequency domain signals. Furthermore, the duration time of FDIAs is detected by the local outlier factor. Then, utilizing the results from SE-DCNN, HVDC ancillary service control framework is reorganized and an HVDC attack defense control is proposed for suppressing the potential influence of various types of FDIAs on the HVDC system ancillary service. Different experiments results demonstrate that the proposed method has the ability to significantly mitigate the frequency deviation and oscillation under the FDIA.

42 ENGINEERING↗
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 OSTI2020

Effects of Jacobian Matrix Regularization on the Detectability of Adversarial Samples

The well-known vulnerability of Deep Neural Networks to adversarial samples has led to a rapid cycle of increasingly sophisticated attack algorithms and proposed defenses. While most contemporary defenses have been shown to be vulnerable to carefully configured attacks, methods based on gradient regularization and out-of-distribution detection have attracted much interest recently by demonstrating higher resilience to a broad range of attack algorithms. However, no study has yet investigated the effect of combining these techniques. In this paper, we consider the effect of Jacobian matrix regularization on the detectability of adversarial samples on the CIFAR-10 image benchmark dataset. We find that regularization has a significant effect on detectability, and in some cases can make an undetectable attack on a baseline model detectable. In addition, we give evidence that regularization may mitigate the known weaknesses of detectors to high-confidence adversarial samples. The defenses we consider here are highly generalizable, and we believe they will be useful for further investigations to transfer machine learning robustness to other data domains.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

Using virtual sensors to accommodate industrial asset control systems during cyber attacks

In some embodiments, an industrial asset may be associated with a plurality of monitoring nodes, each monitoring node generating a series of monitoring node values over time that represent operation of the industrial asset. A threat detection computer may determine that an attacked monitoring node is currently being attacked. Responsive to this determination, a virtual sensor coupled to the plurality of monitoring nodes may estimate a series of virtual node values for the attacked monitoring node(s) based on information received from monitoring nodes that are not currently being attacked. The virtual sensor may then replace the series of monitoring node values from the attacked monitoring node(s) with the virtual node values. Note that in some embodiments, virtual node values may be estimated for a particular node even before it is determined that the node is currently being attacked.

Mestha, Lalit Keshav↗
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↗
DOE OSTI2026

Unsupervised Detection of SOC Spoofing in OCPP 2.0.1 EV Charging Communication Protocol Using One-Class SVM

The electric vehicles (EVs) market keeps growing globally; thus, it is critical to secure the EV charging communication protocols in order to guarantee reliable and fair charging operations among the customers. The Open Charge Point Protocol (OCPP) 2.0.1 supports the communication between the Electric Vehicle Supply Equipment (EVSE) and Charging Station Management Systems (CSMSs); therefore, it becomes vulnerable to several types of attacks, which aim to jeopardize smart charging, billing, and energy management. Specifically, OCPP 2.0.1 allows the self-reporting of the State of Charge (SOC) values, which makes it vulnerable to spoofing-based cyberattacks, which target manipulating the scheduling priorities, distorting the load forecasts, and extending the charging sessions in an unfair manner. In this paper, we try to address this type of attack by providing a comprehensive analysis of the SOC spoofing attacks and introducing a novel unsupervised detection framework based on the One-Class Support Vector Machine (OCSVM) algorithm. Specifically, two types of attack scenarios are analyzed (i.e., priority manipulation and session extension) by deriving engineered features that capture the nonlinear relationships under normal charging behavior. Detailed simulation-based results are derived by utilizing the DESL-EPFL Level 3 EV charging dataset. Our results demonstrate high F1-score and recall in identifying spoofed SOC values and that the proposed OCSVM model demonstrates superior performance compared to alternative clustering and deep-learning based detectors.

EV charging↗
DOE OSTI2022

Precursor Analysis Report: Remote Access Attack on Oldsmar Water Treatment Facility 2021

The Remote Access Attack on Oldsmar Water Treatment Facility 2021 Precursor Analysis Report leverages publicly available information about the Oldsmar cyber attack and catalogs anomalous observables for each technique employed in the attack. This analysis is based upon the methodology of the Cybersecurity for the Operational Technology Environment (CyOTE) program. On 5 February 2021, an adversary gained unauthorized remote access to Bruce T. Haddock Water Treatment Plant in Oldsmar, Florida, which provides treated water to 15,000 customers. The adversary accessed the facility’s Supervisory Control and Data Acquisition (SCADA) workstation and human machine interface (HMI) to change the chemical concentration of sodium hydroxide, commonly referred to as lye and used to regulate acidity levels, from 100 parts per million (PPM) to 11,100 PPM. The chemical was raised to lethal levels that if ingested could lead to serious soft tissue damage, burns, or even death. The facility, however, had redundancies and alarms in place to alert personnel of dangerous chemical levels, and facility officials stated it would have taken 24 to 36 hours for the chemical changes to affect the water supply. Researchers and analysts identified six unique techniques utilized during the attack with a total of 23 observables using MITRE ATT&CK® for Industrial Control Systems. The CyOTE program assesses observables accompanying techniques used prior to the triggering event to identify opportunities to detect malicious activity. If observables accompanying the attack techniques are perceived and investigated prior to the triggering event, earlier comprehension of malicious activity can take place. Four of the identified techniques used during the Oldsmar cyber attack were precursors to the triggering event. Analysis identified 21 observables associated with these precursor techniques, 20 of which were assessed to have an increased likelihood of being perceived in the minutes preceding the triggering event. The response and comprehension time could have been reduced if the observables had been identified earlier. The information gathered in this report contributes to a library of observables tied to a repository of artifacts, data sources, and technique detection references for practitioners and developers to support the comprehension of indicators of attack. Asset owners and operators can use these products if they experience similar observables or to prepare for comparable scenarios.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Precursor Analysis Report: JBS Foods Ransomware Attack 2021

The JBS Foods 2021 Ransomware Attack Precursor Analysis Report leverages publicly available information about the JBS ransomware cyber attack and catalogs anomalous observables for each technique employed in the attack. This analysis is based upon the methodology of the Cybersecurity for the Operational Technology Environment (CyOTE) program. In late May 2021, one of the world’s largest meat producers, JBS Foods, announced they had fallen victim to a worldwide ransomware attack, later found to be REvil ransomware. In the United States alone, JBS Foods accounts for nearly 25% of beef and roughly 20% of pork production. Adversaries initially launched a Distributed Denial of Service (DDoS) attack on the company’s Information Technology (IT) networks in Australia, but the attack impacted operations in Brazil, Canada, and the United States, as well. The attack caused plant operations in all four countries to shut down for at least one day. All nine of the U.S. meatpacking plants temporarily shut down because of the attack. The adversaries initially demanded a $\$$22 million ransom for the company’s data, but later negotiated the ransom down to $\$$11 million even after JBS Foods restored most of their systems. JBS Foods eventually paid the $\$$11 Million for reassurance from the adversaries that none of their customers’ data would be compromised in the future. Despite its short duration, the attack still caused large stocks of meat to spoil. The incident also underscored how adversaries can simultaneously compromise and move laterally through global subsidiaries of an organization. Researchers and analysts identified 22 unique techniques (in a sequence of 21 steps) utilized during the attack with a total of 361 observables using MITRE ATT&CK® for Industrial Control Systems. The CyOTE program assesses observables accompanying techniques used prior to the triggering event to identify opportunities to detect malicious activity. If observables accompanying the attack techniques are perceived and investigated prior to the triggering event, earlier comprehension of malicious activity can take place. Sixteen of the identified techniques used during the JBS Foods cyber attack were precursors to the triggering event. Analysis identified 308 observables associated with these precursor techniques, 163 of which were assessed to have an increased likelihood of being perceived in the 75 days preceding the triggering event. The response and comprehension time could have been reduced if the observables had been identified earlier. The information gathered in this report contributes to a library of observables tied to a repository of artifacts, data sources, and technique detection references for practitioners and developers to support the comprehension of indicators of attack. Asset owners and operators can use these products if they experience similar observables or to prepare for comparable scenarios.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Precursor Analysis Report: SQL Slammer Worm Infection of Davis-Besse Nuclear Power Plant 2003

The SQL Slammer Worm Infection of Davis-Besse Nuclear Power Plant 2003 Precursor Analysis Report leverages publicly available information about Davis-Besse’s 2003 cyber attack and catalogs anomalous observables for each technique employed in the attack. This analysis is based upon the methodology of the Cybersecurity for the Operational Technology Environment (CyOTE) program. On 25 January 2003, the SQL Slammer worm infected more than 90% of vulnerable hosts and crashed the internet in 10 to 15 minutes, making it one of the fastest spreading worms in history. SQL Slammer is a fileless, memory-resident worm that remotely exploits a stack-based buffer overflow vulnerability on local hosts to intensively scan and rapidly self-propagate across the internet. The worm infected approximately 300,000 unpatched hosts running Microsoft Structured Query Language (SQL) Server 2000 or Microsoft Desktop Engine (MSDE) 2000 with SQL Server Resolution Service. The SQL Slammer worm indirectly infected FirstEnergy’s Davis-Besse nuclear power plant by first infecting a consultant’s company network server and then propagating through an external misconfigured connection into Davis-Besse’s site network. The infection caused major network congestion, slow performance, data overloads, and the inability of local hosts to communicate with each other, which eventually caused a loss of availability and a loss of view when the Safety Parameter Display System (SPDS) and Plant Process Computer (PPC) crashed. At the time of the infection, the plant was already offline, the digital monitoring systems had redundant analog backups, and the plant control and safety functions were not affected, so there were no concerns of a safety breach. However, this incident resulted in many lessons learned and spawned important discussions about cybersecurity’s role in nuclear safety and electric power reliability regulation, policy, and guidance. Researchers and analysts identified 10 unique techniques utilized during the attack with a total of 640 observables using MITRE ATT&CK® for Industrial Control Systems. The CyOTE program assesses observables accompanying techniques used prior to the triggering event to identify opportunities to detect malicious activity. If observables accompanying the attack techniques are perceived and investigated prior to the triggering event, earlier comprehension of malicious activity can take place. Eight of the identified techniques used during Davis-Besse cyber attack were precursors to the triggering event. Analysis identified 596 observables associated with these precursor techniques, 428 of which were assessed to have an increased likelihood of being perceived in the 331 days preceding the triggering event. The response and comprehension time could have been reduced if the observables had been identified earlier. The information gathered in this report contributes to a library of observables tied to a repository of artifacts, data sources, and technique detection references for practitioners and developers to support the comprehension of indicators of attack. Asset owners and operators can use these products if they experience similar observables or to prepare for comparable scenarios.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

Precursor Analysis Report: Blackmatter Ransomware Attack on New Cooperative 2021

The BlackMatter Ransomware Attack on New Cooperative 2021 Precursor Analysis Report leverages publicly available information about the New Cooperative cyber attack and catalogs anomalous observables for each technique employed in the attack. This analysis is based upon the methodology of the Cybersecurity for the Operational Technology Environment (CyOTE) program. The BlackMatter ransomware was first identified in July 2021 and is reported to have infected more than 50 corporations around the world. , The Iowa-based grain cooperative, New Cooperative, was impacted by the BlackMatter ransomware on or before 18 September 2021. The adversary likely resided on New Cooperative’s networks for 15 days prior to encrypting its network and demanding New Cooperative pay $5.9 million in ransom by 25 September to unlock systems and prevent 1 terabyte (TB) of sensitive data from being publicly released. It is not clear if New Cooperative paid the ransom. The full impact of the ransomware attack is not known; however, according to New Cooperative’s general manager, the attack caused the company’s automated processes to revert back to processes used in the 1970s. , As of 6 October, only 50 percent of New Cooperative’s operations were utilizing automated processes. The company took eight weeks to rebuild the entire network and information technology (IT) systems from the ground up, which puts the date of fully recovery around 13 November. Researchers and analysts identified 20 unique techniques utilized during the attack with a total of 404 observables using MITRE ATT&CK® for Industrial Control Systems. The CyOTE program assesses observables accompanying techniques used prior to the triggering event to identify opportunities to detect malicious activity. If observables accompanying the attack techniques are perceived and investigated prior to the triggering event, earlier comprehension of malicious activity can take place. Seventeen of the identified techniques used during the New Cooperative cyber attack were precursors to the triggering event. Analysis identified 360 observables associated with these precursor techniques, 284 of which were assessed to have an increased likelihood of being perceived in the 15 days preceding the triggering event. The response and comprehension time could have been reduced if the observables had been identified earlier. The information gathered in this report contributes to a library of observables tied to a repository of artifacts, data sources, and technique detection references for practitioners and developers to support the comprehension of indicators of attack. Asset owners and operators can use these products if they experience similar observables or to prepare for comparable scenarios.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Precursor Analysis Report: Industroyer Targeting Ukraine Electric Power Transport Utility (Ukrenergo) 2016

The Industroyer Targeting Ukraine Electric Power Transport Utility (Ukrenergo) 2016 Precursor Analysis Report leverages publicly available information about the December 2016 cyber attack against the Ukrainian Ukrenergo electric transmission utility and catalogs anomalous observables for each technique employed in the attack. This analysis is based upon the methodology of the Cybersecurity for the Operational Technology Environment (CyOTE) program. Industroyer is a modular malware framework designed to deploy several Industrial Control System (ICS) protocol-specific attack payloads to disrupt electricity distribution. Adversaries deployed Industroyer within the target network on a Microsoft Windows endpoint capable of directly manipulating or communicating with ICS. Industroyer abuses the functionality of a targeted ICS’s legitimate control system to achieve its intended impact. Adversaries likely first gained access to Ukrenergo enterprise networks in early 2016 after a successful spearphishing campaign against organizations in the electric power sector. Adversaries then began capturing credentials beginning on 1 December 2016. This allowed access to the ICS environment at the Pivnichna electric transmission substation outside Kyiv through a device dual-homed on the Information Technology (IT) and ICS networks. Adversaries conducted discovery, targeting, and access to this device using information and previously captured credentials from compromised enterprise IT machines. Finally, the adversaries deployed and launched the Industroyer malware just before midnight on 17 December. By midnight, Ukrenergo had lost control of a targeted substation, resulting in electric power outages for over an hour in the city of Kyiv and the Kyiv region. Researchers and analysts identified 31 unique techniques (used in a sequence of 33 steps) utilized during the attack with a total of 846 observables using MITRE ATT&CK® for Industrial Control Systems. The CyOTE program assesses observables accompanying techniques used prior to the triggering event to identify opportunities to detect malicious activity. If observables accompanying the attack techniques are perceived and investigated prior to the triggering event, earlier comprehension of malicious activity can take place. Twenty-nine of the identified techniques used during the Industroyer cyber attack were precursors to the triggering event. Analysis identified 548 observables associated with these precursor techniques, 353 of which were assessed to have an increased likelihood of being perceived in the 300 days preceding the triggering event. The response and comprehension time could have been reduced if the observables had been identified earlier. The information gathered in this report contributes to a library of observables tied to a repository of artifacts, data sources, and technique detection references for practitioners and developers to support the comprehension of indicators of attack. Asset owners and operators can use these products if they experience similar observables or to prepare for comparable scenarios.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗