Developing Optimal Causal Cyber-Defence Agents via Cyber Security Simulation
TL
TL;DR
This paper proposes that DCBO can act as a blue agent when provided with a view of a simulated network and a causal model of how a red agent spreads within that network, and demonstrates a complete cyber-simulation system used to generate observational data for DCBO.
Abstract
In this paper we explore cyber security defence, through the unification of a novel cyber security simulator with models for (causal) decision-making through optimisation. Particular attention is paid to a recently published approach: dynamic causal Bayesian optimisation (DCBO). We propose that DCBO can act as a blue agent when provided with a view of a simulated network and a causal model of how a red agent spreads within that network. To investigate how DCBO can perform optimal interventions on host nodes, in order to reduce the cost of intrusions caused by the red agent. Through this we demonstrate a complete cyber-simulation system, which we use to generate observational data for DCBO and provide numerical quantitative results which lay the foundations for future work in this space.
References24 items
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Bayesian Optimization
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Deep Reinforcement Learning for Cybersecurity Threat Detection and Protection: A Review
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Intrusion Prevention Through Optimal Stopping
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Dynamic Causal Bayesian Optimization
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