Bayesian Stackelberg games for cyber-security decision support
本文提出一个网络安全决策支持系统，通过贝叶斯斯塔克尔伯格博弈选择最优安全控制组合来应对多阶段攻击，其在线优化方案比传统方法更高效且能显著提升安全效果。
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.