# Bayesian and Multi-Objective Decision Support for Incident Mitigation in Cyber-Physical Systems

arXiv: https://arxiv.org/abs/2509.00770 · HTML: https://mlsanity.com/paper/2509.00770/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2025.

## Built on by (most cited first, 1 of 1)

- [From Incomplete Architecture to Quantified Risk: Multimodal LLM-Driven Security Assessment for Cyber-Physical Systems](https://mlsanity.com/paper/2604.05674/) (2026)

## Abstract (arXiv)

Cyber-physical systems increasingly rely on interconnected physical and digital systems whose security incidents can escalate rapidly into safety and operational failures. Existing decision-support approaches struggle to support incident response because they rely on static assumptions, incomplete vulnerability data, and single-objective risk models that do not adequately capture trade-offs between attack success likelihood, impact severity, and system availability. This paper proposes an adaptive decision-support framework for incident mitigation in cyber-physical systems that integrates hierarchical Bayesian Network modelling, confidence-calibrated exposure estimation, and multi-objective optimisation into a unified, adaptive pipeline. The framework constructs probabilistic models from system architecture and vulnerability data, incorporating complementary vulnerability scores under epistemic uncertainty as conservative, uncertainty-aware reporting metrics for supporting downstream risk assessment. Mitigation strategies are explored as countermeasure portfolios and refined using multi-objective optimisation to identify Pareto-optimal trade-offs suitable for incident response scenarios. Frequency-based heuristics are applied to prioritise mitigation actions across optimisation runs. The framework is evaluated on three representative cyber-physical attack scenarios, demonstrating its ability to adapt to evolving threats and provide actionable decision support under operational constraints, with the aim of enhancing the resilience of cyber-physical systems.