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View all- Li JZhang MLi NWeyns DJin ZTei K(2024)Generative AI for Self-Adaptive Systems: State of the Art and Research RoadmapACM Transactions on Autonomous and Adaptive Systems10.1145/368680319:3(1-60)Online publication date: 30-Sep-2024
A Game-Theoretical Self-Adaptation Framework for Securing Software-Intensive Systems
Authors: 
Nianyu Li, Mingyue Zhang, Jialong Li, Sridhar Adepu, Eunsuk Kang, Zhi JinAuthors Info & Claims
Nianyu Li, Mingyue Zhang, Jialong Li, Sridhar Adepu, Eunsuk Kang, Zhi JinAuthors Info & ClaimsArticle No.: 12, Pages 1 - 49
Abstract
Security attacks present unique challenges to the design of self-adaptation mechanism for software-intensive systems due to the adversarial nature of the environment. Game-theoretical approaches have been explored in security to model malicious behaviors and design reliable defense for the system in a mathematically grounded manner. However, modeling the system as a single player, as done in prior works, is insufficient for the system under partial compromise and for the design of fine-grained defensive policies where the rest of the system with autonomy can cooperate to mitigate the impact of attacks. To address such issues, we propose a new self-adaptation framework incorporating Bayesian game theory and model the defender (i.e., the system) at the granularity of components. Under security attacks, the architecture model of the system is automatically translated, by the proposed translation process with designed algorithms, into a multi-player Bayesian game. This representation allows each component to be modeled as an independent player, while security attacks are encoded as variant types for the components. By solving for pure equilibrium (i.e., adaptation response), the system’s optimal defensive strategy is dynamically computed, enhancing system resilience against security attacks by maximizing system utility. We validate the effectiveness of our framework through two sets of experiments using generic benchmark tasks tailored for the security domain. Additionally, we exemplify the practical application of our approach through a real-world implementation in the Secure Water Treatment System to demonstrate the applicability and potency in mitigating security risks.
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Publication History
Published: 20 April 2024
Online AM: 22 March 2024
Accepted: 13 March 2024
Revised: 08 March 2024
Received: 27 July 2023
Published in TAAS Volume 19, Issue 2
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View all- Li JZhang MLi NWeyns DJin ZTei K(2024)Generative AI for Self-Adaptive Systems: State of the Art and Research RoadmapACM Transactions on Autonomous and Adaptive Systems10.1145/368680319:3(1-60)Online publication date: 30-Sep-2024
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