Network Security under Heterogeneous Cyber-Risk Profiles and Contagion
- URL: http://arxiv.org/abs/2601.16805v1
- Date: Fri, 23 Jan 2026 14:56:24 GMT
- Title: Network Security under Heterogeneous Cyber-Risk Profiles and Contagion
- Authors: Elisa Botteghi, Martino S. Centonze, Davide Pastorello, Daniele Tantari,
- Abstract summary: This paper introduces a cyber-risk management framework for networked digital systems.<n>We address the problem of optimally allocating cybersecurity resources across a network.<n>We propose risk measures based on contagion paths and analyze how propagation dynamics influence optimal defense strategies.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Cyber risk has become a critical financial threat in today's interconnected digital economy. This paper introduces a cyber-risk management framework for networked digital systems that combines the strategic behavior of players with contagion dynamics within a security game. We address the problem of optimally allocating cybersecurity resources across a network, focusing on the heterogeneous valuations of nodes by attackers and defenders, some areas may be of high interest to the attacker, while others are prioritized by the defender. We explore how this asymmetry drives attack and defense strategies and shapes the system's overall resilience. We extend a method to determine optimal resource allocation based on simple network metrics weighted by the defender's and attacker's risk profiles. We further propose risk measures based on contagion paths and analyze how propagation dynamics influence optimal defense strategies. Numerical experiments explore risk versus cost efficient frontiers varying network topologies and risk profiles, revealing patterns of resource allocation and cyber deception effects. These findings provide actionable insights for designing resilient digital infrastructures and mitigating systemic cyber risk.
Related papers
- Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report v1.5 [61.787178868669265]
This technical report presents an updated and granular assessment of five critical dimensions: cyber offense, persuasion and manipulation, strategic deception, uncontrolled AI R&D, and self-replication.<n>This work reflects our current understanding of AI frontier risks and urges collective action to mitigate these challenges.
arXiv Detail & Related papers (2026-02-16T04:30:06Z) - A Survey on Reconfigurable Intelligent Surfaces in Practical Systems: Security and Privacy Perspectives [35.265580543159835]
Reconfigurable Intelligent Surfaces (RIS) have emerged as transformative technology capable of reshaping wireless environments through dynamic manipulation of electromagnetic waves.<n>This survey provides a comprehensive examination of RIS applications in real-world systems, with a focus on the security and privacy threats, vulnerabilities, and defensive strategies relevant to practical use.
arXiv Detail & Related papers (2025-12-12T00:54:24Z) - A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents [45.53643260046778]
Recent advances in large language models (LLMs) have catalyzed the rise of autonomous AI agents.<n>These large-model agents mark a paradigm shift from static inference systems to interactive, memory-augmented entities.
arXiv Detail & Related papers (2025-06-30T13:34:34Z) - CyFence: Securing Cyber-Physical Controllers via Trusted Execution Environment [45.86654759872101]
Cyber-physical systems (CPSs) have experienced a significant technological evolution and increased connectivity, at the cost of greater exposure to cyber-attacks.<n>We propose CyFence, a novel architecture that improves the resilience of closed-loop control systems against cyber-attacks by adding a semantic check.<n>We evaluate CyFence considering a real-world application, consisting of an active braking digital controller, demonstrating that it can mitigate different types of attacks with a negligible overhead.
arXiv Detail & Related papers (2025-06-12T12:22:45Z) - Co-evolutionary Dynamics of Attack and Defence in Cybersecurity [7.949437760740007]
This study uses an Evolutionary Game Theory framework to investigate the evolutionary dynamics of attacks and defences in cyberspace.<n>We find that systems with high defence intensities show stability with minimal attack frequencies, whereas low-defence environments show instability, and are vulnerable to attacks.<n>Our analysis suggests that adaptive cybersecurity strategies based on EGT can improve resource allocation, enhance system resilience, and reduce the overall risk of cyberattacks.
arXiv Detail & Related papers (2025-05-25T22:11:24Z) - Modeling Interdependent Cybersecurity Threats Using Bayesian Networks: A Case Study on In-Vehicle Infotainment Systems [0.0]
This paper reviews the application of Bayesian Networks (BNs) in cybersecurity risk modeling.<n>A case study is presented in which a STRIDE-based attack tree for an automotive In-Vehicle Infotainment (IVI) system is transformed into a BN.
arXiv Detail & Related papers (2025-05-14T01:04:45Z) - Frontier AI's Impact on the Cybersecurity Landscape [46.32458228179959]
We find that while AI is already widely used in attacks, its application in defense remains limited.<n>Experts expect AI to continue favoring attackers over defenders, though the gap will gradually narrow.
arXiv Detail & Related papers (2025-04-07T18:25:18Z) - A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments [55.60375624503877]
Model Extraction Attacks (MEAs) threaten modern machine learning systems by enabling adversaries to steal models, exposing intellectual property and training data.<n>This survey is motivated by the urgent need to understand how the unique characteristics of cloud, edge, and federated deployments shape attack vectors and defense requirements.<n>We systematically examine the evolution of attack methodologies and defense mechanisms across these environments, demonstrating how environmental factors influence security strategies in critical sectors such as autonomous vehicles, healthcare, and financial services.
arXiv Detail & Related papers (2025-02-22T03:46:50Z) - Cyber Shadows: Neutralizing Security Threats with AI and Targeted Policy Measures [0.0]
Cyber threats pose risks at individual, organizational, and societal levels.<n>This paper proposes a comprehensive cybersecurity strategy that integrates AI-driven solutions with targeted policy interventions.
arXiv Detail & Related papers (2025-01-03T09:26:50Z) - Cyber Risk Taxonomies: Statistical Analysis of Cybersecurity Risk Classifications [0.0]
We argue in favour of switching the attention from goodness-of-fit and in-sample performance, to focusing on the out-of sample forecasting performance.
Our results indicate that business motivated cyber risk classifications appear to be too restrictive and not flexible enough to capture the heterogeneity of cyber risk events.
arXiv Detail & Related papers (2024-10-04T04:12:34Z) - Decision-Making Frameworks for Network Resilience -- Managing and Mitigating Systemic (Cyber) Risk [44.99833362998488]
We introduce a decision-making framework tailored for the management of systemic risk in networks.
This framework is constructed upon three fundamental components: (1) a set of acceptable network configurations, (2) a set of interventions aimed at risk mitigation, and (3) a cost function quantifying the expenses associated with these interventions.
arXiv Detail & Related papers (2023-12-21T14:29:04Z) - Cyber Risk Assessment for Capital Management [8.807503512479427]
This paper introduces a two-pillar cyber risk management framework to address the pervasive challenges in managing cyber risk.<n>The first pillar, cyber risk assessment, combines insurance frequency-severity models with cybersecurity cascade models to capture the unique nature of cyber risk.<n>The second pillar, cyber capital management, facilitates informed allocation of capital for a balanced cyber risk management strategy.
arXiv Detail & Related papers (2022-05-17T15:25:23Z) - Measurement-driven Security Analysis of Imperceptible Impersonation
Attacks [54.727945432381716]
We study the exploitability of Deep Neural Network-based Face Recognition systems.
We show that factors such as skin color, gender, and age, impact the ability to carry out an attack on a specific target victim.
We also study the feasibility of constructing universal attacks that are robust to different poses or views of the attacker's face.
arXiv Detail & Related papers (2020-08-26T19:27:27Z) - Adversarial Machine Learning Attacks and Defense Methods in the Cyber
Security Domain [58.30296637276011]
This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques.
It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
arXiv Detail & Related papers (2020-07-05T18:22:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.