Automated Power System Fault Prediction and Precursor Discovery Using Multi-Modal Data
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42 ENGINEERING↗
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SBIR Phase I: An IoT-based Mul6-Gas Sensor to Iden6fy Precursors Used to Predict Flameout Events
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03 NATURAL GAS↗
Role of atmospheric resonance and land–atmosphere feedbacks as a precursor to the June 2021 Pacific Northwest Heat Dome event
We demonstrate an indirect, rather than direct, role of quasi-resonant amplification of planetary waves in a summer weather extreme. We find that there was an interplay between a persistent, amplified large-scale atmospheric circulation state and soil moisture feedbacks as a precursor for the June 2021 Pacific Northwest “Heat Dome” event. An extended resonant planetary wave configuration prior to the event created an antecedent soil moisture deficit that amplified lower atmospheric warming through strong nonlinear soil moisture feedbacks, favoring this unprecedented heat event.
54 ENVIRONMENTAL SCIENCES↗
A prediction model of failure threshold for shear deformation in a Zr-based bulk metallic glass
The failure of bulk metallic glasses (BMGs) during plastic deformation at room temperature is abrupt and instantaneous, while the analysis of precursor information based on avalanche events helps predict catastrophic failure. An acoustic emission (AE) signal can provide accurate precursor information for material failure, due to its sensitive and high fast calculation ability. In the current study, AE monitoring tests are carried out during uniaxial compression tests of BMGs at different strain rates. The AE experimental failure threshold, E max , is proposed on the basis of AE cumulative energy, which reflects the intensity of damage evolution at different loading conditions. Compared with the critical shear band velocity (CSBV) associated with stick-slip dynamics of serrated flow, E max is a more sensitive failure parameter since it is connected with the local microscopic changes that occur during the material response process. Here, the E max is obtained prior to reaching the CSBV since the calculation of these two avalanches analysis focuses on the different stages of shear band growth. In particular, AE events are related to the “dry” friction process in the first stage, however, the CSBV is responsible for the “viscous” glide in the second stage. Therefore, Emax is not affected by the complex interactions between the shear bands during the stick-slip process. The maximum avalanche of serrated flow, S max , is proposed as the experimental failure threshold, which depends on the applied strain rate as S max ~ $\dot{ε}$ –λ . According to the relationship of E max and S max , the theoretical failure threshold, E max , follows a criterion E max = 2545$\dot{ε}$ –λ - 4468, where λ is equivalent to 0.15 for this work. Finally, combining the different calculations and AE measurements, this model gives new insights to predict the deformation failure behavior of Zr-based BMGs.
36 MATERIALS SCIENCE↗
Advancing the Limits of InSAR to Detect Crustal Displacement from Low-Magnitude Earthquakes through Deep Learning
Detecting surface deformation associated with low-magnitude (M w ≤ 5) seismicity using interferometric synthetic aperture radar (InSAR) is challenging due to the subtlety of the signal and the often challenging imaging environments. However, low-magnitude earthquakes are potential precursors to larger seismic events, and thus characterizing the crustal displacement associated with them is crucial for regional seismic hazard assessment. We combine InSAR time-series techniques with a Deep Learning (DL) autoencoder denoiser to detect the magnitude and extent of crustal deformation from the M w = 3.4 Gallina, New Mexico earthquake that occurred on 30 July 2020. Although InSAR alone cannot detect event-related deformation from such a low-magnitude seismic event, application of the DL method reveals maximum displacements as small as (±2.5 mm) in the vicinity of both the fault and earthquake epicenter without prior knowledge of the fault system. This finding improves small-scale displacement discernment with InSAR by an order of magnitude relative to previous studies. We additionally estimate best-fitting fault parameters associated with the observed deformation. The application of the DL technique unlocks the potential for low-magnitude earthquake studies, providing new insights into local fault geometries and potential risks from higher-magnitude earthquakes. This technique also permits low-magnitude event monitoring in areas where seismic networks are sparse, allowing for the possibility of global fault deformation monitoring.
58 GEOSCIENCES↗
A Weakly Supervised Machine Learning Procedure for Magnet Quench Diagnostics
Voltage taps remain the standard and reliable diagnostic tool for detecting quenches in superconducting magnets. However, they identify a quench only at the time of voltage rise and do not provide information on earlier physical precursors. In this work, we investigate whether acoustic emission data can reveal precursor activity that occurs before conventional voltage detection using machine learning techniques. We introduce an event selection method and a weakly supervised machine learning procedure to learn data-driven criteria for identifying potential acoustic precursors to quenches. Two Convolutional Neural Network (CNN) architectures are trained: one on acoustic sensor events from our selection procedure and one on the Fast Fourier Transforms (FFTs) of these events. Both networks are trained iteratively using confidence-weighted loss functions to associate certain subsets of training data with a precursor label. We evaluate the performance of these models by examining the time distribution of events classified as potential precursors relative to the quench onset. Results indicate that the proposed approach can possibly distinguish acoustic emission events occurring closer to the quench from earlier acoustic activity during ramping, suggesting the potential for flagging quench precursors in acoustic data.
Khan, Maira [Fermilab] (ORCID:0009000891602387)↗
Micro Baselines for Operational Technology Environments
Critical infrastructure stakeholders need to baseline their networks to understand expected communications. Top-down approaches to baselining rely on observables that are generally available but lack properties upon which traditional statistical tools depend. We propose to construct micro-baselines: signatures within operational networks based on observables associated with specific events. Such observables are informed by precursor analysis reports of historical cyber attacks on operational environments developed by Cybersecurity for Operational Technology Environments (CyOTE). Baseline measurements depend upon context beyond the cyber domain. An energy plant's baseline running in the summer may statistically differ from a similar facility in a colder region. Domain knowledge must be integrated to apply general micro-baselining algorithms to a facility-specific context. Therefore, we propose to explore the feasibility of transferring micro baselining algorithms across different facilities. Facilities that implement the same processes in different geographic locations will be compared relative to observable measurements used in micro-baselining for comparable events. One evaluation approach would condition or augment dynamic observables measured within a facility network testbed with additional observables derived from geographic context or infrastructure dependencies such as those provided by the All-Hazards Analysis tool.
97 MATHEMATICS AND COMPUTING↗
Forming a database to study reversed magnetic shear from the National Spherical Torus eXperiment using machine learning
Achieving a long-lived reversed magnetic shear (RMS) target plasma in the National Spherical Torus eXperiment Upgrade will require developing various sustainment scenarios. To help with the ongoing plasma control efforts, the development of a new analysis for the motional Stark effect (MSE) diagnostic using a machine learning algorithm, namely, MSE-ML, is described. MSE-ML will be used to identify patterns during RMS discharges, some of which suffer magnetohydrodynamic (MHD) events resulting in current redistribution and monotonic q-profiles. A database consisting of q and magnetic shear profiles is being constructed primarily based on the existing National Spherical Torus eXperiment data with equilibrium reconstructions constrained by the magnetic field pitch angle profile measured using the multi-channel MSE diagnostic. An unsupervised k-means clustering of the data is developed to study the RMS formation as a function of time. The initial clustering from the q-profiles shows significant differences in both amplitude and the duration of the RMS period. As a goal, the clustering results that detect and distinguish shots with substantial and sustained RMS are to be used as a preprocessing step in a supervised algorithm to identify the underlying conditions that lead to long-lasting improved confinement with RMS. Another aim of the MSE-ML study is to identify precursors of RMS-destroying MHD events in either derived data such as the q-profile or directly measured data such as the magnetic field pitch angle profile.
Uzun-Kaymak, I. U. (ORCID:0000000276251493)↗
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↗
Precursor Analysis Report: Doppelpaymer Ransomware Attack on Petroleos Mexicanos (PEMEX) 2019
The DoppelPaymer Ransomware Attack on Petroleos Mexicanos (PEMEX) 2019 Precursor Analysis Report leverages publicly available information about the PEMEX 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 2019 DoppelPaymer ransomware attack on PEMEX, Mexico’s nationalized petroleum corporation, highlights a unique threat that ransomware and cybercriminal extortion poses to Operational Technology (OT) environments in critical infrastructure. The incident began with an employee downloading commodity malware that allowed adversaries to gain initial access to PEMEX’s enterprise environment. After conducting privilege escalation, tool ingress, and data exfiltration, the adversaries deployed DoppelPaymer ransomware throughout the PEMEX enterprise environment, resulting in the company having to take dozens of systems offline for at least several days. Although PEMEX stated that their operations were not affected, the data exfiltrated from PEMEX was made available for download on DoppelPaymer’s leak site, as well as on other illicit criminal forums. This stolen data included not only company information, but also sensitive OT-specific configuration data. This incident showcases how cybercriminal exfiltration and posting of sensitive OT architecture documentation can pose security concerns for the targeted organization for years due to the long lifespan of OT assets and architectures. Researchers and analysts identified 18 unique techniques utilized during the attack with a total of 190 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. Fifteen of the identified techniques used during the DoppelPaymer ransomware attack were precursors to the triggering event. Analysis identified 163 observables associated with these precursor techniques, 34 of which were assessed to have an increased likelihood of being perceived in the 60 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↗
Precursor Analysis Report: Ryuk Ransomware Attack on Universal Health Services 2020
The Ryuk Ransomware Attack on Universal Health Services (UHS) 2020 Precursor Analysis Report leverages publicly available information about the 2020 UHS 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. UHS manages over 400 hospitals and is one of the largest healthcare providers in the United States with 3.5 million patients each year. On 27 September 2020, UHS suffered a widespread ransomware attack that resulted in a denial of service to critical internet-dependent healthcare systems including workstations, phones, and data centers. Employees resorted to filing patient details with pen and paper, while other facilities had to redirect ambulances and urgent patients to other facilities for adequate care. Adversaries carried out the attack with Ryuk, a ransomware that encrypts data and generates a RyukReadMe.txt ransom note with the ransom fee to decrypt the data, varying from 15 Bitcoin (BTC) to 50 BTC, equivalent to roughly $\$$353,892 to $\$$964,617. UHS did not pay the ransom and was able to recover data through backups, but still reported an impact of $\$$67 million dollars in recovery costs. On 29 October, one month after the attack, UHS made an official statement that their systems had been restored and they were resuming normal operations. Researchers and analysts identified 18 unique techniques (used in a sequence of 19 steps) utilized during the attack with a total of 185 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. Fourteen of the identified techniques used during the UHS cyber attack were precursors to the triggering event. Analysis identified 106 observables associated with these precursor techniques, 82 of which were assessed to have an increased likelihood of being perceived in the 30 days to two hours 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↗
Precursor Analysis Report: Cyber Attack on Thyssenkrupp Blast Furnace 2014
The Cyber Attack on Thyssenkrupp Blast Furnace 2014 Precursor Analysis Report leverages publicly available information about the Thyssenkrupp Steel Mill 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 December 2014, the German Government’s Federal Office for Information Security (BSI) released a report detailing a cyber attack on a German steel mill that occurred earlier that year, though exact dates and details of the attack were not revealed. While the report did not specify the name of the company, multiple sources identified the victim as one of Europe’s largest steel manufacturers, Thyssenkrupp AG. Further, Thyssenkrupp announced on 16 May of that year that Europe’s largest blast furnace, “Schwelgern 2,” located at its facility in Duisburg, Germany, would be offline for several weeks for repairs and upgrades, suggesting Schwelgern 2 was likely the target of the attack. The attack began in early 2014, when adversaries infiltrated the victim steel mill’s Information Technology (IT) network via a spearphishing campaign, then worked their way into the Operational Technology (OT) environment, where they executed software that caused denial of service, denial of control, and eventually a loss of control. This led to the blast furnace shutting down without proper safety procedures, resulting in catastrophic physical damage. No lives were lost in the incident, but ThyssenKrupp suffered $4 million in damage to the blast furnace and an additional $6 million in lost revenue. The adversaries required specialized knowledge and expertise in steel production, which enabled them to compromise a variety of internal systems and components across both IT and OT networks. The attack also demonstrated detailed knowledge of the industrial control systems (ICS) and production processes being used. This combination resulted in one of the earliest known publicly reported cybersecurity incidents resulting in physical damage to ICS equipment. Researchers and analysts identified 19 unique techniques (used in a sequence of 20 steps) utilized during the attack with a total of 454 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. Fifteen of the identified techniques used during the Thyssenkrupp cyber attack were precursors to the triggering event. Analysis identified 369 observables associated with these precursor techniques, 316 of which were assessed to have an increased likelihood of being perceived in the 120 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↗
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↗
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↗
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↗
Precursor Analysis Report: Industroyer2 and Wiper Malware Targeting Ukrainian Energy Provider 2022
The Industroyer2 and Wiper Malware Targeting Ukrainian Energy Provider 2022 Precursor Analysis Report leverages publicly available information about the Industroyer2 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. An adversary attempted to cause a blackout in Ukraine in April 2022 by using the Industroyer2 malware against a regional Ukrainian energy provider. The adversary targeted eight high-voltage electrical substations and utilized the malware in tandem with disk wipers for Windows, Linux, and Solaris operating systems in an attempt to make response and recovery efforts more difficult. The adversary reused a piece of the original Industroyer malware designed to open circuit breakers and de-energize target substations. The adversary gained initial access to the victim’s enterprise network through unknown means in February 2022 and was able to perform reconnaissance, pivot to the operations network, and reside in the system for at least 51 days. This gave the adversary a detailed understanding of the environment and allowed them to customize the Industroyer2 malware to the victim’s operations network. However, defenders detected and stopped the attack before the adversary could achieve their intended impact. Had the Industroyer2 attack been successful, it could have caused a blackout for more than two million people during the early stages of Russia’s invasion of Ukraine. Researchers and analysts identified 22 unique techniques (used in a sequence of 31 steps) utilized during the attack with a total of 297 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-three of the identified techniques used during the Industroyer2 cyber attack were precursors to the triggering event. Analysis identified 224 observables associated with these precursor techniques, 122 of which were assessed to have an increased likelihood of being perceived in the 51 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↗
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↗
Zircon-to-reidite phase transition enhanced by minor radiation damage: Implications for hypervelocity impacts
Reidite, a high-pressure phase of zircon, is increasingly identified at terrestrial impact sites. Despite its growing recognition, the potential applications for estimating minimum impact pressure face impediments due to existing discrepancies in the condition of zircon-reidite transformation, controversial models governing the transformation mechanism, and unclear effects of pre-existing radiation damage on reidite formation. Here, we show enhanced reidite formation by synchrotron X-ray diffraction, Raman spectroscopy, and transmission electron microscopy analyses of zircon grains that have experienced different alpha-decay doses from U and Th impurities and subsequent pressurization in diamond anvil cells. Below ~1 × 10 18 α-decay events/g, the α-decay-induced isolated point defects in the still crystalline zircon facilitate the minor atomic readjustments required for reidite formation. However, above this dose, the loss of long-range periodicity in severely damaged or even metamict zircon inhibits the transformation. The enhanced reidite formation by minor radiation damage coincides with the more common occurrence of reidite at impact sites for which the precursor zircon has a relatively lower alphadecay- event dose before the impact event. In addition, the detailed atomic-scale structures of twinned reidite provide unambiguous evidence for a characteristic internal stress-induced martensitic transition. Furthermore, these findings have important implications for interpreting the formation conditions of natural reidite due to the convergence of pressure from the static, shockwave, and natural reidite samples.
58 GEOSCIENCES↗