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Igor Linkov

Publications and source records attributed to Igor Linkov.

At least 19 recordsLinked to original sources
arXiv2026

Planning for Mission Efficiency, Robustness, and Resilience for Unmanned Autonomous Systems (UASs) in Contested Environments

Unmanned Autonomous Systems (UASs) can remotely sense targets across large-scale areas but may be vulnerable to threats in contested environments. Current approaches consider efficiency through UAS path planning, but missions prioritizing robustness (i.e., the capacity to withstand disruptions) and resilience (i.e., the ability to recover from disruptions) are rarely considered despite their ability to increase mission success. This paper presents robust and resilient strategies for route planning for a fleet of UASs in a heterogeneous threat landscape. By integrating components of robustness and resilience in route planning through allowing the UASs to overlap in the waypoint locations and dynamic mission adaptation, the probability of mission success of complete area coverage can be increased. The results show that a robustness-focused strategy shows significant improvement in probability of mission success over an efficiency-based strategy, and that a resilience-based strategy outperforms both the efficiency- and robustness- based strategies. The specified methodologies provide promising future directions of research to explicitly incorporate robustness and resilience into UAS mission planning.

physics.soc-ph↗
arXiv2025

Reverse Stress Testing for Supply Chain Resilience

Supply chains' increasing globalization and complexity have recently produced unpredictable disruptions, ripple effects, and cascading resulting failures. Proposed practices for managing these concerns include the advanced field of forward stress testing, where threats and predicted impacts to the supply chain are evaluated to harden the system against the most damaging scenarios. Such approaches are limited by the almost endless number of potential threat scenarios and cannot capture residual risk. In contrast to forward stress testing, this paper develops a reverse stress testing (RST) methodology that allows to predict which changes, with probabilistic certainty, across the supply chain network are most likely to cause a specified level of disruption at a specific entity in the network. The methodology was applied to the case of copper wire imports into the USA, a simple good which may have significant implications for national security. Results show that Canada, Chile, and Mexico are predicted to consistently be sources of disruptions at multiple loss levels. Other countries (e.g., Papua New Guinea) may contribute to small disruptions but be less important for the catastrophic losses of concern for decision makers. Other countries' disruptions would be catastrophic (e.g., Chile). The proposed methodology is the first case of reverse stress testing application in complex multilayered supply chains and can be used to address both risk and resilience.

physics.data-an↗
arXiv2025

Contested logistics: Resilience of strategic highways and railways

Military logistics rely heavily on public infrastructure, such as highways and railways, to transport troops, equipment, and supplies, linking critical installations through the Department of Defense's Strategic Highway Network and Strategic Rail Corridor Network. However, these networks are vulnerable to disruptions that can jeopardize operational readiness, particularly in contested environments where adversaries employ non-traditional threats to disrupt logistics, even within the homeland. This paper presents a contested logistics model that utilizes network science and Geographic Information System (GIS) to evaluate the robustness and resilience of strategic transportation networks under various disruption scenarios. By integrating GIS data to model logistics networks, simulating disruptions, and quantifying their impacts, we identified vulnerabilities in US power projection routes and assessed the resilience and robustness of highways and railways. Our findings reveal that highways are more resilient than railways, with greater capacity to absorb targeted disruptions. These findings underscore the importance of prioritizing investments in highway infrastructure and reinforcing vulnerable road and rail segments, particularly in high-risk regions, to enhance the resilience of military logistics and maintain operational effectiveness in contested conditions.

physics.soc-ph↗
arXiv2025

Threat-Agnostic Resilience: Framing and Application for Critical Infrastructure

Critical infrastructure is not indestructible. Threats, both emergent and systemic, have propagated beyond historical norms that risk mitigation efforts alone cannot alleviate. Interdependencies between infrastructural systems compound consequences at vulnerable locations but can be harnessed to maximize operational efficiency and recovery capability. Characterizing practical forms of resilient infrastructure through 5 basic principles (modularity, distributedness, redundancy, diversity, and plasticity) provides a foundation for critical infrastructure systems to absorb, recover, and adapt to disruptions agnostic of threat source. Challenges exist in developing methodological foundations for these principles within practical applications to prevent sunk cost and over-constraining operational procedures. This study evaluates each principle, establishing practical forms, quantification strategies, and their impact on critical infrastructure resilience.

physics.soc-ph↗
arXiv2024

Assessing the Robustness and Resilience of U.S. Strategic Highways: A Network Science Perspective

Network science is a powerful tool for analyzing transportation networks, offering insights into their structures and enabling the quantification of resilience and robustness. Understanding the underlying structures of transportation networks is crucial for effective infrastructure planning and maintenance. In military contexts, network science is valuable for analyzing logistics networks, critical for the movement and supply of troops and equipment. The U.S. Army's logistical success, particularly in the "fort-to-port" phase, relies heavily on the Strategic Highway Network (STRAHNET) in the U.S., which is a system of public highways crucial for military deployments. However, the shared nature of these networks with civilian users introduces unique challenges, including vulnerabilities to cyberattacks and physical sabotage, which is highlighted by the concept of contested logistics. This paper proposes a method using network science and geographic information systems (GIS) to assess the robustness and resilience of transportation networks, specifically applied to military logistics. Our findings indicate that while the STRAHNET is robust against targeted disruptions, it is more resilient to random disruptions.

physics.soc-ph↗
arXiv2024

The Convergence of AI and Synthetic Biology: The Looming Deluge

The convergence of artificial intelligence (AI) and synthetic biology is rapidly accelerating the pace of biological discovery and engineering. AI techniques, such as large language models and biological design tools, are enabling the automated design, build, test, and learning cycles for engineered biological systems. This convergence promises to democratize synthetic biology and unlock novel applications across domains from medicine to environmental sustainability. However, it also poses significant risks around reliability, dual use, and governance. The opacity of AI models, the deskilling of workforces, and the outdated nature of current regulatory frameworks present challenges in ensuring responsible development. Urgent attention is needed to update governance structures, integrate human oversight into increasingly automated workflows, and foster a culture of responsibility among the growing community of bioengineers. Only by proactively addressing these issues can we realize the transformative potential of AI-driven synthetic biology while mitigating its risks.

q-bio.OT↗
arXiv2024

Access to Emergency Services: A New York City Case Study

Emergency services play a crucial role in safeguarding human life and property within society. In this paper, we propose a network-based methodology for calculating transportation access between emergency services and the broader community. Using New York City as a case study, this study identifies 'emergency service deserts' based on the National Fire Protection Association (NFPA) guidelines, where accessibility to Fire, Emergency Medical Services, Police, and Hospitals are compromised. The results show that while 95% of NYC residents are well-served by emergency services, the residents of Staten Island are disproportionately underserved. By quantifying the relationship between first responder travel time, Emergency Services Sector (ESS) site density, and population density, we discovered a negative power law relationship between travel time and ESS site density. This relationship can be used directly by policymakers to determine which parts of a community would benefit the most from providing new ESS locations. Furthermore, this methodology can be used to quantify the resilience of emergency service infrastructure by observing changes in accessibility in communities facing threats.

physics.soc-ph↗
arXiv2022

Finding shortest and nearly shortest path nodes in large substantially incomplete networks

Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions (PPI) and the Internet at the autonomous system (AS) level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks. We demonstrate the utility of latent-geometric path-finding in problems of cellular pathway reconstruction and communication security.

physics.soc-ph↗
arXiv2022

Cybertrust: From Explainable to Actionable and Interpretable AI (AI2)

To benefit from AI advances, users and operators of AI systems must have reason to trust it. Trust arises from multiple interactions, where predictable and desirable behavior is reinforced over time. Providing the system's users with some understanding of AI operations can support predictability, but forcing AI to explain itself risks constraining AI capabilities to only those reconcilable with human cognition. We argue that AI systems should be designed with features that build trust by bringing decision-analytic perspectives and formal tools into AI. Instead of trying to achieve explainable AI, we should develop interpretable and actionable AI. Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. In doing so, it will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making and ensure broad benefits from deploying and advancing its computational capabilities.

cs.AI↗
arXiv2022

Autonomous Cyber Defense Introduces Risk: Can We Manage the Risk?

From denial-of-service attacks to spreading of ransomware or other malware across an organization's network, it is possible that manually operated defenses are not able to respond in real time at the scale required, and when a breach is detected and remediated the damage is already made. Autonomous cyber defenses therefore become essential to mitigate the risk of successful attacks and their damage, especially when the response time, effort and accuracy required in those defenses is impractical or impossible through defenses operated exclusively by humans. Autonomous agents have the potential to use ML with large amounts of data about known cyberattacks as input, in order to learn patterns and predict characteristics of future attacks. Moreover, learning from past and present attacks enable defenses to adapt to new threats that share characteristics with previous attacks. On the other hand, autonomous cyber defenses introduce risks of unintended harm. Actions arising from autonomous defense agents may have harmful consequences of functional, safety, security, ethical, or moral nature. Here we focus on machine learning training, algorithmic feedback, and algorithmic constraints, with the aim of motivating a discussion on achieving trust in autonomous cyber defenses.

cs.CR↗
arXiv2022

Cyber Resilience: by Design or by Intervention?

The term "cyber resilience by design" is growing in popularity. Here, by cyber resilience we refer to the ability of the system to resist, minimize and mitigate a degradation caused by a successful cyber-attack on a system or network of computing and communicating devices. Some use the term "by design" when arguing that systems must be designed and implemented in a provable mission assurance fashion, with the system's intrinsic properties ensuring that a cyber-adversary is unable to cause a meaningful degradation. Others recommend that a system should include a built-in autonomous intelligent agent responsible for thinking and acting towards continuous observation, detection, minimization and remediation of a cyber degradation. In all cases, the qualifier "by design" indicates that the source of resilience is somehow inherent in the structure and operation of the system. But what, then, is the other resilience, not by design? Clearly, there has to be another type of resilience, otherwise what's the purpose of the qualifier "by design"? Indeed, while mentioned less frequently, there exists an alternative form of resilience called "resilience by intervention." In this article we explore differences and mutual reliance of resilience by design and resilience by intervention.

cs.CR↗
arXiv2021

Cyber Resilience in IoT network: Methodology and example of assessment through epidemic spreading

Cyber Resilience is an important property of complex systems and is important consideration in developing specific IoT applications. This work aims at introducing a novel approach to assess IoT resilience adopting the risk perception in network based epidemic spreading approach. In particular IoT has been considered a network of devices where the probability of infection and interactions (communication), needs to be balanced in order to reduce the malware outbreack while maintaining the network functionalities at an acceptable level. The mathematical model and the simulation results reveal the benefit of a shift from a risk-based to a resilience based approach to threat management in IoT.

cs.NI↗
arXiv2021

Relationship among state reopening policies, health outcomes and economic recovery through first wave of the COVID-19 pandemic in the U.S

State governments in the U.S. have been facing difficult decisions involving tradeoffs between economic and health-related outcomes during the COVID-19 pandemic. Despite evidence of the effectiveness of government-mandated restrictions mitigating the spread of contagion, these orders are stigmatized due to undesirable economic consequences. This tradeoff resulted in state governments employing mandates in widely different ways. We compare the different policies states implemented during periods of restriction (lockdown) and reopening with indicators of COVID-19 spread and consumer card spending at each state during the first wave of the pandemic in the U.S. between March and August 2020. We find that while some states enacted reopening decisions when the incidence rate of COVID-19 was minimal or sustained in its relative decline, other states relaxed socioeconomic restrictions near their highest incidence and prevalence rates experienced so far. Nevertheless, all states experienced similar trends in consumer card spending recovery, which was strongly correlated with reopening policies following the lockdowns and relatively independent from COVID-19 incidence rates at the time. Our findings suggest that consumer card spending patterns can be attributed to government mandates rather than COVID-19 incidence in the states. We estimate the recovery in states that reopened in late April was more than the recovery in states that did not reopen in the same period - 15% for consumer card spending and 18% for spending by high income households. This result highlights the important role of state policies in minimizing health impacts while promoting economic recovery and helps planning effective interventions in subsequent waves and immunization efforts.

econ.GN↗
arXiv2021

To Improve Cyber Resilience, Measure It

We are not very good at measuring -- rigorously and quantitatively -- the cyber security of systems. Our ability to measure cyber resilience is even worse. And without measuring cyber resilience, we can neither improve it nor trust its efficacy. It is difficult to know if we are improving or degrading cyber resilience when we add another control, or a mix of controls, to harden the system. The only way to know is to specifically measure cyber resilience with and without a particular set of controls. What needs to be measured are temporal patterns of recovery and adaptation, and not time-independent failure probabilities. In this paper, we offer a set of criteria that would ensure decision-maker confidence in the reliability of the methodology used in obtaining a meaningful measurement.

cs.CR↗
arXiv2021

How to Measure Cyber Resilience of an Autonomous Agent: Approaches and Challenges

Several approaches have been used to assess the performance of cyberphysical systems and their exposure to various types of risks. Such assessments have become increasingly important as autonomous attackers ramp up the frequency, duration and intensity of threats while autonomous agents have the potential to respond to cyber-attacks with unprecedented speed and scale. However, most assessment approaches have limitations with respect to measuring cyber resilience, or the ability of systems to absorb, recover from, and adapt to cyberattacks. In this paper, we provide an overview of several common approaches, discuss practical challenges and propose research directions for the development of effective cyber resilience measures.

cs.CR↗
arXiv2020

The Vaccine Supply Chain: A Call for Resilience Analytics to Support COVID-19 Vaccine Production and Distribution

The COVID19 pandemic has highlighted the lack of resilience in supply chains, as global networks fail from disruptions at single nodes and connections. Through an overview of the existing vaccine and pharmaceutical supply chain publications focusing on resilience, as well as recent papers reporting modeling of resilience in supply chains across multiple fields, we find that models for supply chain resilience are few and most of them are focused on individual dimensions of resilience rather than on comprehensive strategy necessary for scaling up vaccine production and distribution in emergency settings. We find that COVID19 resulted in a wave of interest to supply chain resilience, but publications from 2020 are narrow in focus and largely qualitative in nature; evidence-based models and measures are rare. Further, publications often focus exclusively on specific portions of the specific supply chain of interest, excluding associated supporting networks, such as transportation, social and command and control (C2) necessary for vaccine production and equitable distribution. This lack of network analysis is a major gap in the literature that needs to be bridged in order to create methods of real-time analysis and decision tools for the COVID19 vaccine supply chain. We conclude that a comprehensive, quantitative approach to network resilience that encompasses the supply chain in the context of other social and physical networks is needed in order to address the emerging challenges of a large-scale COVID-19 vaccination program. We further find that the COVID-19 pandemic underscores the necessity of positioning supply chain resilience within a multi-network context and formally incorporating temporal dimensions into analysis through the NAS definition of resilience, plan, absorb, recover, adapt, to ensure essential needs are met across all dimensions of society.

physics.soc-ph↗
arXiv2020

Value-based optimization of healthcare resource allocation for COVID-19 hot spots

With the emerging COVID-19 crisis, a critical task for public health officials and policy makers is to decide how to prioritize, locate, and allocate scarce resources. To answer these questions, decision makers need to be able to determine the location of the required resources over time based on emerging hot spot locations. Hot spots are defined as concentrated areas with sharp increases in COVID19 cases. Hot spots place stress on existing healthcare resources, resulting in demand for resources potentially exceeding current capacity. This research will describe a value based resource allocation approach that seeks to coordinate demand, as defined by uncertain epidemiological forecasts, with the value of adding additional resources such as hospital beds. Value is framed as a function of the expected usage of a marginal resource (bed, ventilator, etc). Subject to certain constraints, allocation decisions are operationalized using a nonlinear programming model, allocating new hospital beds over time and across a number of geographical locations. The results of the research show a need for a value based approach to assist decision makers at all levels in making the best possible decisions in the current highly uncertain and dynamic COVID environment.

physics.soc-ph↗