Work

MT2M: Strategic Cost-Based Optimization of Cyber Defense in Variable Constraints Systems

Year: 2026

Type: article

Abstract: When confronted with advanced cyber threats, traditional cybersecurity methods often struggle with system performance and cost-effectiveness. Current approaches using game theory for passive applications, such as Moving Target Defense (MTD), lack flexibility in adapting to varying resource criticalities and rely on rigid cost assumptions that overlook the interdependencies among system components. Additionally, these approaches are complex and grow exponentially more complex with the network. This paper presents the \ac{model}, a strategic \ac{mtd} framework based on Bayesian Stackelberg game theory to optimize cyber defense costs. Optimization is done while considering both resource criticality and node capacity. By transforming a complex, NP-hard cost problem into a linear one, \ac{model} enables scalable deployment. Numerical simulations demonstrate that the proposed framework achieves comparable security to traditional methods while reducing defense costs by up to $15\%$. This research establishes a framework that can be later expanded to include a more variable and complex network configuration.

Source: Journal of Network and Systems Management

DOI: 10.1007/s10922-026-10068-z

Authors: Jamil Ahmad Kassem, Helena Rifà Pous, Joaquin Garcia-Alfaro

Institutions: Universitat Oberta de Catalunya, Institut Polytechnique de Paris, Télécom SudParis, Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux

Language: English

Topic: Information and Cyber Security

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,090