# System Component-Level Self-Adaptations for Security via Bayesian Games

arXiv: https://arxiv.org/abs/2103.08673 · HTML: https://mlsanity.com/paper/2103.08673/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 12 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [A Game-Theoretic Taxonomy and Survey of Defensive Deception for Cybersecurity and Privacy](https://mlsanity.com/paper/1712.05441/) (2017)
- [Learning-Guided Network Fuzzing for Testing Cyber-Physical System Defences](https://mlsanity.com/paper/1909.05410/) (2019)
- [Control Behavior Integrity for Distributed Cyber-Physical Systems](https://mlsanity.com/paper/1812.08310/) (2018)
- [MORPH: A Reference Architecture for Configuration and Behaviour Self-Adaptation](https://mlsanity.com/paper/1504.08339/) (2015)

## Abstract (arXiv)

Security attacks present unique challenges to self-adaptive system design due to the adversarial nature of the environment. However, modeling the system as a single player, as done in prior works in security domain, is insufficient for the system under partial compromise and for the design of fine-grained defensive strategies where the rest of the system with autonomy can cooperate to mitigate the impact of attacks. To deal with such issues, we propose a new self-adaptive framework incorporating Bayesian game and model the defender (i.e., the system) at the granularity of components in system architecture. The system architecture model is translated into a Bayesian multi-player game, where each component is modeled as an independent player while security attacks are encoded as variant types for the components. The defensive strategy for the system is dynamically computed by solving the pure equilibrium to achieve the best possible system utility, improving the resiliency of the system against security attacks.