Journal
DECISION SUPPORT SYSTEMS
Volume 148, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.dss.2021.113599
Keywords
Attack graphs; Bayesian Stackelberg games; Cyber-security; Security games; Security investment
Categories
Funding
- EPSRC [EP/R004897/1]
Ask authors/readers for more resources
This decision support system for cyber-security utilizes preventive optimization, learning mechanisms, and online optimization to select security controls to counteract multi-stage attacks, proving to be more efficient and providing significant security improvements.
A decision support system for cyber-security is here presented. The system aims to select an optimal portfolio of security controls to counteract multi-stage attacks. The system has several components: a preventive optimisation to select controls for an initial defensive portfolio, a learning mechanism to estimate possible ongoing attacks, and an online optimisation selecting an optimal portfolio to counteract ongoing attacks. The system relies on efficient solutions of bi-level optimisations, in particular, the online optimisation is shown to be a Bayesian Stackelberg game solution. The proposed solution is shown to be more efficient than both classical solutions like Harsanyi transformation and more recent efficient solvers. Moreover, the proposed solution provides significant security improvements on mitigating ongoing attacks compared to previous approaches. The novel techniques here introduced rely on recent advances in Mixed-Integer Conic Programming (MICP), strong duality and totally unimodular matrices.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available