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Online Identification of IT Systems through Active Causal Learning

arXiv 2509.02130 ↗First posted 2 Sep 2025cs.LG cs.DC cs.NIcited by 0

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Abstract (arXiv)

Identifying a causal model of an IT system is fundamental to many branches of systems engineering and operation. Such a model can be used to predict the effects of control actions, optimize operations, diagnose failures, detect intrusions, etc., which is central to achieving the longstanding goal of automating network and system management tasks. Traditionally, causal models have been designed and maintained by domain experts. This, however, proves increasingly challenging with the growing complexity and dynamism of modern IT systems. In this paper, we present the first principled method for online, data-driven identification of an IT system in the form of a causal model. The method, which we call active causal learning, estimates causal functions that capture the dependencies among system variables in an iterative fashion using Gaussian process regression based on system measurements, which are collected through a rollout-based intervention policy. We prove that this method is optimal in the Bayesian sense and that it produces effective interventions. Experimental validation on a testbed shows that our method enables accurate identification of a causal system model while inducing low interference with system operations.

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References · 21

  1. 2019Combinatorial Bayesian Optimization using the Graph Cartesian Productcited by 78
  2. 2020Active Learning for Nonlinear System Identification with Guaranteescited by 78
  3. 2020Causal Bayesian Optimizationcited by 39
  4. 2022Developing Optimal Causal Cyber-Defence Agents via Cyber Security Simulationcited by 37
  5. 2022Active Bayesian Causal Inferencecited by 34
  6. 2022Active Learning for Optimal Intervention Design in Causal Modelscited by 30
  7. 2022Model-based Causal Bayesian Optimizationcited by 26
  8. 2021Intrusion Prevention through Optimal Stoppingcited by 18
  9. 2021Dynamic Causal Bayesian Optimizationcited by 17
  10. 2020Multi-task Causal Learning with Gaussian Processescited by 14
  11. 2023Learning Near-Optimal Intrusion Responses Against Dynamic Attackerscited by 13
  12. 2024Optimal Defender Strategies for CAGE-2 using Causal Modeling and Tree Searchcited by 12
9 more
  1. 2025Incident Response Planning Using a Lightweight Large Language Model with Reduced Hallucinationcited by 10
  2. 2021Measuring the robustness of Gaussian processes to kernel choicecited by 6
  3. 2025Adaptive Network Security Policies via Belief Aggregation and Rolloutcited by 6
  4. 2023Functional Causal Bayesian Optimizationcited by 5
  5. 2025Online Incident Response Planning under Model Misspecification through Bayesian Learning and Belief Quantizationcited by 5
  6. 2017Probabilistic Active Learning of Functions in Structural Causal Modelscited by 3
  7. 2022Rollout Algorithms and Approximate Dynamic Programming for Bayesian Optimization and Sequential Estimationcited by 3
  8. 2025Optimal Security Response to Network Intrusions in IT Systemscited by 3
  9. 2021Active Learning of Continuous-time Bayesian Networks through Interventionscited by 2

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