[Paper Review] Attack-Defense Quantification Based On Game-Theory

Su Yang, Yuqing Zhang+1 more|arXiv (Cornell University)|Feb 27, 2019

Information and Cyber Security26 references4 citations

TL;DR

This paper proposes an attack-defense stochastic game model (ADSGM) to quantitatively evaluate cyber security behaviors using game theory. By modeling attacker and defender interactions with distinct defense mechanisms and computing utility-based payoffs, the framework enables accurate assessment of security posture, demonstrated through a case study showing active defense effectiveness and attack exposure risks.

ABSTRACT

With the developing of the attack and defense technology, the cyber environment has been more and more sophisticated. We failed to give an accurate evaluation of network security situation, as we lack a more accurate quantitative evaluation of attack-defense behaviors. In response to this situation, we proposed an attack-defense stochastic game model (ADSGM), analyzed the different security property of distinct defense mechanism, and put forward a corresponding utility calculation coping with the distinct defense mechanism. Through a case study, we showed the impact of active defense and the risk of attack exposure, demonstrated the effectiveness of our methods on attack-defense behavior quantification. This paper filled with the gap in the quantitative assessment of defensive measures, to make the quantitative evaluation of attack-defense more comprehensive and accurate.

Motivation & Objective

• To address the lack of quantitative evaluation methods for cyber defense mechanisms in complex, evolving attack environments.
• To develop a formal model that captures the dynamic interplay between attackers and defenders using game theory.
• To quantify the utility of different defense mechanisms under varying attack strategies.
• To enable comprehensive, accurate assessment of attack-defense behavior beyond qualitative metrics.
• To demonstrate the model's effectiveness through a real-world case study on active defense and attack exposure.

Proposed method

• Formulates an attack-defense stochastic game model (ADSGM) to represent strategic interactions between attackers and defenders.
• Introduces distinct defense mechanisms and assigns unique utility functions to model their effectiveness.
• Applies game-theoretic equilibrium analysis to derive optimal strategies for both attacker and defender.
• Uses case study simulations to evaluate the impact of active defense and exposure to attack.
• Computes utility values based on defense type, attack success probability, and system state transitions.
• Employs a probabilistic framework to model uncertainty in attack and defense outcomes.

Experimental results

Research questions

• RQ1 How can attack-defense interactions be modeled as a stochastic game to enable quantitative security evaluation?
• RQ2 What are the utility differences across various defense mechanisms under strategic attack scenarios?
• RQ3 How does active defense influence the overall security posture in a dynamic threat environment?
• RQ4 What is the risk of attack exposure when defense mechanisms are deployed?
• RQ5 Can game-theoretic modeling improve the accuracy of cyber security situation assessment?

Key findings

• The proposed ADSGM enables precise quantification of security outcomes by modeling strategic interactions between attackers and defenders.
• Different defense mechanisms yield significantly different utility values, demonstrating their relative effectiveness.
• Active defense was shown to reduce attack success probability and improve system resilience in the case study.
• The model quantifies the risk of attack exposure when defense mechanisms are insufficient or poorly timed.
• The utility-based framework provides a comprehensive, measurable approach to evaluating cyber defense strategies.
• The case study confirms the model's practical applicability in real-world security scenario analysis.