Jukka Soikkeli
Abstract
Cyber-attacks cost businesses millions of dollars every year, a key component of which is the cost of business disruption from system downtime. As cyber-attacks cannot all be prevented, there is a need to consider the cyber resilience of systems, i.e. the ability to withstand cyber-attacks and recover from them.
Previous works discussing system cyber resilience typically either offer generic high-level guidance on best practices, provide limited attack modelling, or apply to systems with special characteristics. There is a lack of an approach to system cyber resilience evaluation that is generally applicable yet provides a detailed consideration for the system-level impacts of cyber-attacks and defences.
We propose a methodology for evaluating the effectiveness of actions intended to improve resilience to cyber-attacks, considering their impacts on system output performance, and monetary costs. It is intended for analysing attacks that can disrupt the system function, and involves modelling attack progression, system output production, response to attacks, and costs from cyber-attacks and defensive actions.
Studies of three use cases demonstrate the implementation and usefulness of our methodology. First, in our redundancy planning study, we considered the effect of redundancy additions on mitigating the impacts of cyber-attacks on system output performance. We found that redundancy with diversity can be effective in increasing resilience, although the reduction in attack-related costs must be balanced against added maintenance costs. Second, our work on attack countermeasure selection shows that by considering system output impacts across the duration of an attack, one can find more cost-effective attack responses than without such considerations. Third, we propose an approach to mission viability analysis for multi-UAV deployments facing cyber-attacks, which can aid resource planning and determining if the mission can conclude successfully despite an attack. We provide different implementations of our model components, based on use case requirements.