Testing the international mechanism for responding to an asteroid impact threat
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Attack Chain Estimator (ACE) Application Overview The Attack Chain Estimator (ACE) Application is a sophisticated tool designed for the ingestion, classification, sequencing, and enrichment of cybersecurity threat reports. This application leverages advanced machine learning models and extensive historical data to provide comprehensive insights into cyber threats, specifically targeting Industrial Control Systems (ICS). Purpose The primary functions of the ACE Application include: Ingestion of Cybersecurity Threat Reporting: Capable of ingesting text-based threat reports in markdown or text file format. Supports ingestion of structured data from other sources in STIX/JSON format. Classification of Report’s Text-Based Events: Utilizes a DeBERTa classifier, specifically trained on cybersecurity data, to map the events to MITRE ATT&CK for ICS Tactics and Techniques. Classification is performed using multiple Jupyter notebooks and machine learning workflows hosted as FastAPI microservices: regex_data deberta_base_35_train_hft_classifier_mlflow.ipynb hft_regex_classifier_mlflow.ipynb param_train_hft_classifier_mlflow.ipynb regex_tactic_tech.ipynb Ordering of Tactics, Techniques, and Observable Events: Sequences the identified tactics, techniques, and events to form a coherent attack chain. Enrichment with Historical Attack Chain Details: Enhances the attack chain with details from historical attacks using a Markov model developed from CyOTE Precursor Analysis Report data. The Markov model is available as a FastAPI endpoint for seamless integration. Enrichment with Adversary Emulation Capabilities Data: Integrates adversary emulation capabilities data using MITRE Caldera for OT adversary abilities UUIDs. Export of Output Files: Provides options to export the enriched attack chain in JSON or CSV formats. Routing of Output to Other Applications: Facilitates routing of output to various platforms and applications, including: Threat Intelligence Platforms COREII Scout for Threat Intelligence Analysis COREII Modeling and Simulation for Adversary Emulation Technical Description The ACE Application is an advanced cybersecurity tool designed to provide detailed threat analysis and sequence generation. It is built on a robust architecture that integrates natural language processing, machine learning, and historical data modeling. Key Components: Data Ingestion Module: Handles the input of threat reports and data from various formats, ensuring flexibility in data sources. Classification Engine: Employs DeBERTa-based classifiers hosted as FastAPI microservices to analyze and classify threat report events in accordance with the MITRE ATT&CK framework for ICS. Sequence Generator: Orders the classified events into a logical attack chain, providing clear insight into the sequence of tactics and techniques used in the threat. Enrichment Engine: Integrates historical data and adversary emulation capabilities to enhance the attack chain with valuable context and additional details. The historical data enrichment is powered by a Markov model, which is available as a FastAPI endpoint. Export and Routing Module: Facilitates the export of the enriched attack chain in multiple formats and routes the output to designated applications for further analysis or emulation.
Threat intelligence sources are numerous, and the quality of their intelligence is equally variable. The need for quality, actionable, and shareable intelligence is becoming increasingly important as cyber-threats continue to increase. In this paper, we describe the process we used to enrich U.S. CERT’s Technical Alert entitled “Russian Government Cyber-Activity Targeting Energy and Other Critical Infrastructure Sectors,” affectionately referred to as “Russians In the Grid,” from a series of separate but related reports to a final, actionable, unified Structured Threat Information Expression (STIX) v2 representation of the report. In the process, we detail the tools and procedures we used, some of which are automated and others highly manual. We identify some of the limitations of current STIX enrichment tools and techniques, and propose enhancements to allow for more effective enrichment and better overall threat intelligence to be used for more effective threat data-sharing, as well as machine learning-based predictive threat work being conducted at Idaho National Laboratory.
Currently, nuclear power plant physical security systems are highly dependent on air-gaps as a protective measure against cyber-threats. Cyber-physical threats become more likely as advanced cyber-threat capabilities to jump air-gaps transition into common use. Defending against the emerging threat of cyber-enabled physical intrusions is poorly understood. The consequence of these cyber-physical attacks has no quantitative analysis method to inform risk-informed, performance-based cybersecurity approaches. By modifying the physical security simulation tool Dante, cyber-physical threat consequence was able to be analyzed on a notional facility. The results of this analysis are used to design a Defensive Cybersecurity Architecture (DCSA) for the physical security system to produce example resilience measures for this notional facility. A DCSA defines security levels to provide a graded approach for defending plant functions, and security zones for trusted communication between systems. This approach can be applied to real world systems to produce physical protection systems and response measures that are resilient to cyber-physical threats.
Several recent near-miss encounters with asteroids and comets have focused attention on the threat of a catastrophic impact with the Earth. This document reviews the historical impact record and current understanding of the number and location of Near Earth Objects (NEO’s) to address their impact probability. Various ongoing projects intended to survey and catalog the NEO population are also reviewed. Details are then given of an MSFC-led study, intended to develop and assess various candidate systems for protection of the Earth against NEO’s. An existing program, used to model the NEO threat, was extensively modified and is presented here. Details of various analytical tools, developed to evaluate the performance of proposed technologies for protection against the NEO threat, are also presented. Trajectory tools, developed to model the outbound path a vehicle would take to intercept or rendezvous with a target asteroid or comet, are described. Also, details are given of a tool that was created to model both the un-deflected inbound path of an NEO as well as the modified, post-deflection, path. The number of possible options available for protection against the NEO threat was too numerous for them to all be addressed within the study; instead, a representative selection were modeled and evaluated. The major output from this work was a novel process by which the relative effectiveness of different threat mitigation concepts can be evaluated during future, more detailed, studies. In addition, several new or modified mathematical models were developed to analyze various proposed protection systems. A summary of the major lessons learned during this study is presented, as are recommendations for future work. It is hoped that this study will serve to raise the level attention about this very real threat and also demonstrate that successful defense is both possible and practicable, provided appropriate steps are taken.
The secure transportation of high-consequence materials, including nuclear and radiological assets, is a critical global priority in the face of escalating terrorism, security threats, and violent protests targeting these operations. Effective surveillance detection—the ability to identify, assess, and respond to potential threats across a continuum of scenarios—is paramount in addressing these challenges. This paper outlines a phased, multi-tiered training program designed to strengthen the surveillance detection capabilities of organizations responsible for nuclear material transport. The proposed training program adopts a progressive approach, gradually increasing in technical complexity to provide participants with comprehensive knowledge and tools for implementing robust security strategies. It targets a wide spectrum of stakeholders, including competent authorities, regulators, inspectors, shippers, carriers, law enforcement, and emergency response personnel, equipping them to plan, evaluate, and safeguard nuclear material transportation effectively. Each phase of the program emphasizes distinct elements of the surveillance detection continuum and transport security, focusing on critical topics such as threat identification, adversary task timelines, protective methodologies, and attack mitigation strategies. The training framework is anchored in technical exchanges and scenario-driven courses that reflect real-world complexities and challenges. By addressing the surveillance detection continuum comprehensively—from early threat assessment to active countermeasures—the program reinforces global efforts to secure nuclear assets. It aligns with international security objectives and fosters a strong security culture within participating organizations, ensuring personnel are prepared to counter potential threats and maintain the safe, secure movement of these materials. Ultimately, this initiative aims to enhance preparedness, security, and response capabilities, supporting the global mission to safeguard high-consequence materials against evolving threats.
Based on its experience in building assessment tools, Argonne National Laboratory (Argonne) has demonstrated expertise in developing comparative methodologies and question sets that provide both a physical and cyber-based risk assessment for the commercial facilities sector. A high-level risk assessment tool can be beneficial for venues and organizations that fall under a wide array of facility characterizations, ultimately accommodating the evolving landscape of threats specific to these facilities. The Health Analysis and Research for Public Events (HARPE) Tool is an assessment that seeks to help facility managers assess and mitigate aspects of safety, security, and emergency management within their venues. With the pandemic featuring prominently in today’s landscape, we have adapted and extended the scope of the HARPE tool so that it offers users the ability to integrate assessments of their preparation for, response to, and recovery from threats faced at the venue arising from the COVID-19 pandemic. Traditional threats and hazards remain prevalent in today’s landscape, and the best defense is a proactive approach to preventing and mitigating these threats. At the very core of prevention and mitigation is the first step of conducting a risk assessment—this is the gap that HARPE fills. While it is obvious that the response to COVID-19 is health and sanitation driven, leaders within the commercial facilities sector must also know where to best extend their efforts in mitigating future threats arising from the pandemic. Public events and mass gatherings are directly impacted by COVID-19; however, with the help of HARPE, leaders can focus their attention on core impact areas related to the safety and security of returning to a new normal, as they assess their risks using the Argonne-developed tool. HARPE provides an important solution to the COVID-19 situation by assisting venue managers and health officials in identifying their risks.
Electric vehicles (EVs) and charging infrastructure are networked systems, which employ high-level communications in support of charging and grid service decisions. Public key cryptography (PKC) underlies much of the security and privacy protections of the information exchange. We are entering a new epoch where quantum computing threats must be seriously considered. A sufficiently large quantum computer, so named Cryptographically Relevant Quantum Computer (QRQC), will be able to perform the mathematical operations to efficiently attack the underpinnings of traditional PKC, thus jeopardizing the digital foundations for trust, communications security, and data security. Estimates suggest a QRQC can break public key encryption and digital signatures in the manner of tens to hundreds of hours, compared to traditional computing that would demand more than 10 18 years in a brute force-style attack. A consensus belief of quantum theorists, quantum experimenters, and cryptographers suggest that the quantum threat will be likely realized in the next twenty years. To address the threat, post-quantum cryptography, which is cryptosystems that are designed to be secure against both traditional and quantum computing threats, must be adopted. Migration from traditional PKC to quantum-resilient cryptography is a global undertaking and likely represents the largest transition in computing history. The nascent state of EV public key infrastructure, combined with limited adoption of the vehicle secure charging features, presents an opportunity to establish a preference for quantum-resistant cryptography as a step on the migration path. Delays will stunt the efforts as rapidly accelerating EVs sales and huge infrastructure investments will create large growing bases of long-lived vehicles and infrastructure. Migration preparations can commence while NIST continues the process to standardize post-quantum cryptography (PQC), which are quantum-resilient algorithms designed to be secure against traditional and quantum computing threats. The first step in preparing EV charging is to identify the presence of traditional public key cryptography algorithms and applications. With this objective in mind, this report is intended to advise the vehicle manufacturers, charging station manufacturers, charging station operators, charge network providers and other EV charging stakeholders with information on traditional PKC application and the potential risks when PKC becomes insecure. This report, the first in a series of reports discussing the topics existing at the confluence of post-quantum cryptography adoption and EV charging, identifies traditional public key applications employed and identifies potential consequences of leaving EV charging infrastructure vulnerable to quantum computing. The focus remains squarely on the of EV charging and infrastructure with respect to PKC and is believed by the authors to complement the NIST SP 1800-38 Migration to Post-Quantum Cryptography. While the report is centered on infrastructure, there are implications to vehicles.
As King et al succinctly write, “Collision between the Earth and small objects in the solar system are known to occur regularly, and can result in extinction-level events at their most energetic or more commonly widespread loss of life or economic destruction even for relatively low-energy impacts.” Currently, the JPL Sentry System and the NASA NEO Surveyor mission track, identify, and monitor potential asteroid threats. The JPL Sentry System4 is an automated collision monitoring system which scans the current asteroid catalog for potential impact trajectories with Earth. A 2017 study led by P. Tricarico titled “The near-Earth asteroid population from two decades of observations” analyzes the combined observations of nine leading asteroid surveys. The NASA NEO Surveyor mission seeks to identify 90% of near-Earth objects (NEO) with diameters greater than 140 meters (460 feet) within 10 years. The NEO Surveyor combines data from two infrared imaging detectors to measure NEO size, composition, shape, rotational state, and orbits. Recently, the NASA Double Asteroid Redirection Test (DART) mission successfully altered the orbit of asteroid moon Dimorphos in 2022. This demonstrated that orbit alteration is possible. Thus, collisions between the Earth and asteroids are both likely and demonstrate potential for great harm. There is ongoing work to identify, catalog, and track potential threats. However, when a threat is identified there is not yet a system in place to target the threatening asteroid and defend the planet from potentially existential threats. Building on the DART Mission, we explore a planetary defense system to mitigate asteroid threats.
As NASA has retired the Space Shuttle Program, a new method of transporting compressed gaseous nitrogen and oxygen needed to be created for delivery of these crucial life support resources to the International Space Station (ISS). One of the methods selected by NASA includes the use of highly pressurized, unprotected Recharge Tank Assemblies (RTAs) utilizing Composite Overwrapped Pressure Vessels (COPVs). A COPV consists of a thin liner wrapped with a fiber composite and resin or epoxy. It is typically lighter weight than an all metal pressure vessel of similar volume and therefore provides a higher-efficiency means for gas storage. However COPVs are known to be susceptible to damage resulting from handling, tool drop impacts, or impacts from other objects. As a result, a comprehensive Damage Control Plan has been established to mitigate damage to the RTA COPV throughout its life cycle. The DCP is intended to evaluate and mitigate defined threats during manufacturing, shipping and handling, test, assembly level integration, shipment while pressurized, launch vehicle integration and mission operations by defining credible threats and methods for preventing potential damage while still maintaining the primary goal of resupplying ISS gas resources. A comprehensive threat assessment is performed to identify all threats posed to the COPV during the different phases of its lifecycle. The threat assessment is then used as the basis for creating a series of general inspection, surveillance and reporting requirements which apply across all phases of the COPV's life, targeted requirements only applicable to specific work phases and a series of training courses for both ground personnel and crew aboard the ISS. A particularly important area of emphasis deals with creating DCP requirements for a highly pressurized, large and unprotected RTA COPV for use during Inter Vehicular Activities (IVA) operations in the micro gravity environment while supplying pressurized gas to the ISS for crew life support.
The dual-wall Whipple shield is the shield of choice for lightweight, long-duration flight. The shield uses an initial sacrificial wall to initiate fragmentation and melt an impacting threat that expands over a void before hitting a subsequent shield wall of a critical component. The key parameters to this type of shield are the rear wall and its mass which stops the debris, as well as the minimum pressure generated under threat particle impact of the sacrificial wall and the amount of void that is available for expansion. Ensuring the minimum pressure is sufficiently high to achieve large scale fragmentation/melt of the threat particle enables the expansion of the threat and reduces the momentum flux of the debris on the rear wall. Three key factors in the minimum pressure achieved are the thickness of the sacrificial wall relative to the characteristic dimension of the impacting particle, the density and material cohesion contrast of the sacrificial wall relative to the threat particle and the impact speed. The mass of the rear wall and the sacrificial wall are desirable to minimize for launch costs and dynamic concerns making it important to have an understanding of the effects of density contrast and impact speed. In this paper a fourth key parameter is identified related to fragmentation, which corresponds to the ratio of the size of the projectile relative to the transition from brittle to ductile hole growth in the projectile. Ballistic limit equations have been developed to define the failure limits of a MMOD shield, generally in terms of projectile diameter (or mass), impact velocity, and angle. Within the range of impact velocities relevant for Earth-orbiting spacecraft, three distinct regions of penetration phenomenology have been identified for Whipple shields: center dot Low velocity: the projectile is eroded (and possibly deformed) during its passage through the bumper plate, but is not fragmented. Thus, perforation of the rear wall is by a fragment with a mass and speed equal to or less than the original impactor. center dot Intermediate (shatter) velocity: impact velocities are sufficient to induce projectile fragmentation upon impact with the bumper plate, resulting in a coarse debris cloud with large solid fragments. Increasing velocity within the shatter regime results in increased fragmentation, and eventually melting, of the projectile and bumper fragments, generating a finer and more evenly dispersed debris cloud. Failure of the rear wall is a complicated combination of modes observed at low- and hypervelocity. center dot Hypervelocity: the projectile and holed-out bumper material is completely, or nearly completely, melted and/or vaporized by the initial impact. The resultant debris cloud impacts over a dispersed area of the rear wall, loading it impulsively and inducing failure through rupture or petalling. While each of these regimes are well observed with extensive empirical methods to describe these regions, differences in impactor materials, configurations of shields and questions about the limitations of the attainable impact speeds have left questions that are difficult to answer from completely empirical methods.
The dual-wall, Whipple shield is the shield of choice for lightweight, long-duration flight. The shield uses an initial sacrificial wall to initiate fragmentation and melt an impacting threat that expands over a void before hitting a subsequent shield wall of a critical component. The key parameters to this type of shield are the rear wall and its mass which stops the debris, as well as the minimum shock wave strength generated by the threat particle impact of the sacrificial wall and the amount of room that is available for expansion. Ensuring the shock wave strength is sufficiently high to achieve large scale fragmentation/melt of the threat particle enables the expansion of the threat and reduces the momentum flux of the debris on the rear wall. Three key factors in the shock wave strength achieved are the thickness of the sacrificial wall relative to the characteristic dimension of the impacting particle, the density and material cohesion contrast of the sacrificial wall relative to the threat particle and the impact speed. The mass of the rear wall and the sacrificial wall are desirable to minimize for launch costs making it important to have an understanding of the effects of density contrast and impact speed. An analytic model is developed here, to describe the influence of these three key factors. In addition this paper develops a description of a fourth key parameter related to fragmentation and its role in establishing the onset of projectile expansion.
Effective cybersecurity operations require the ability to analyze large amounts of information to assess security risks and formulate defensive strategies against adversaries. This has become more complex in recent years as the sprawl and interconnectivity of devices grows through implementation of virtualization, cloud computing, and Internet of Things (IoT). The amount of data and analysis required for effective cybersecurity command and control decisions far exceeds humans’ capacity to perform manually. We characterize the analysis problem as cyber situational understanding. The research presented to improve cyber situational understanding focuses on vulnerability analysis and threat intelligence. Regarding vulnerabilities, entities must analyze and plan work for between thousands and tens of thousands of software vulnerabilities annually. Entities heavily use network firewalls to limit vulnerability exposure. As a result, some of these vulnerabilities permit exposure to adversarial exploitation, whereas others are inaccessible and therefore present negligible risk of exploitation. Distinguishing between high and low risk software vulnerabilities requires a deep understanding of the vulnerability, network firewall protection, and characteristics of the targeted device. This problem is solved by extracting network service features from vulnerability data features using both machine-learning and natural language processing. Then, the network firewall topology is parsed to determine which vulnerabilities are reachable by adversaries. Ultimately, a state-based safety analysis ascertains which vulnerabilities are unsafe. A related vulnerability analysis problem occurs in cybersecurity operations when associating an entity’s hardware and software assets to public vulnerability databases. Assets often reveal hardware and software through installation artifacts and network service identification, and entities store these artifacts in inventory databases. However, software and hardware vendors apply a standard Common Platform Enumeration (CPE) naming convention when publicly reporting vulnerabilities. Associating these two datasets often requires many hours to days of manual inspection. The proposed solution automates the mapping approach of human analysts using fuzzy matching techniques, natural language processing, and, ultimately, machine learning to present a small set of recommendations for mapping the two datasets. The result significantly reduces human analysis time and reduces the occurrence of false positives in vulnerability notifications. Finally, cyber threat intelligence (CTI) requires associating cyber observable artifacts, such as IP addresses, URIs, and file hashes, with cyber threat tactics, techniques, and procedures. Unfortunately, most CTI data is compartmentalized across multiple organizations and cannot be shared due to the legal and reputational risk with cyber threat being associated with the entity. The approach to solving this problem inovlves using a distributed ledger with anonymous token spending and authentication. This allows a consortium of semi-trusted entities to share the workload of curating CTI for a threat sharing community’s cooperative benefit.
This presentation, Risk-Based, Graded Approach to Insider Threat Mitigation: Human Measures, introduces a structured framework for managing insider threat risk using internationally recognized guidance from the International Atomic Energy Agency (IAEA) Nuclear Security Series No. 8-G (Rev. 1) and the Joint Statement on Mitigating Insider Threats (INFCIRC/908). The presentation emphasizes that effective insider threat mitigation (ITM) depends on both positional controls, which manage inherent risk based on access, authority, and knowledge, and human measures, which address residual risk reflected in behavior, motivation, and reliability. Using a risk-informed and graded approach, the presentation outlines methods for identifying and prioritizing high-risk positions, applying layered organizational controls, and integrating human reliability mechanisms such as the Behavior Observation Program (BOP), Fitness-for-Duty (FFD) evaluations, Employee Assistance Programs (EAP), and Nuclear Security Culture (NSC). The human-focused portion examines behavioral and organizational indicators of opportunity, vulnerability, motivation, and crisis, demonstrating how early detection, deterrence, and response can prevent insider events. The session concludes with a case review of the Millstone Nuclear Power Station incident involving engineer George Galatis. The case illustrates how weak leadership and a poor safety culture can create conditions for failure and how a comprehensive ITM framework could have altered the outcome. The objective of this presentation is to help practitioners apply a risk-based, graded philosophy to human factors and promote a culture of accountability, communication, and resilience within nuclear organizations.
Grid-interactive efficient buildings (GEBs) are not only exposed to passive threats (e.g., physical faults) but also active threats such as cyber-attacks launched on the network-based control systems. The impact of cyber-attacks on GEB operation are not yet fully understood, especially as regards the performance of grid services. To quantify the consequences of cyber-attacks on GEBs, this paper proposes a modeling and simulation framework that includes different cyber-attack models and key performance indexes to quantify the performance of GEB operation under cyber-attacks. The framework is numerically demonstrated to model and evaluate cyber-attacks such as data intrusion attacks and Denial-of-Service attacks on a typical medium-sized office building that uses the BACnet/IP protocol for communication networks. Simulation results show that, while different types of attacks could compromise the building systems to different extents, attacks via the remote control of a chiller yield the most significant consequences on a building system’s operation, including both the building service and the grid service. It is also noted that a cyber-attack impacts the building systems during the attack period as well as the post-attack period, which suggests that both periods should be considered to fully evaluate the consequences of a cyber-attack.