Developing Optimal Causal Cyber-Defence Agents via Cyber Security Simulation
This addresses cyber defense for network security practitioners, but it is incremental as it applies an existing method (DCBO) to a new domain with a novel simulator.
The paper tackles the problem of cyber security defense by developing a system that uses dynamic causal Bayesian optimization (DCBO) as a blue agent to make optimal interventions on host nodes in a simulated network, reducing intrusion costs from a red agent, with numerical quantitative results provided.
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.