论文标题
攻击影响评估通过通过状态空间扩大确切的凸面化来评估
Attack Impact Evaluation by Exact Convexification through State Space Augmentation
论文作者
论文摘要
我们解决了控制系统安全的攻击影响评估问题。我们将问题作为马尔可夫决策过程提出，并具有时间关节的机会约束，迫使对手避免在整个考虑的时间段内被检测到。由于联合约束，最佳控制策略不仅取决于当前状态，还取决于整个历史记录，这导致了搜索空间的爆炸，因此问题通常是棘手的。结果表明，除了当前状态外，是否已经触发了警报，还足以在每个时间步骤指定最佳决策。将信息扩展到状态空间会引起等效的凸优化问题，该问题可使用标准求解器进行处理。
We address the attack impact evaluation problem for control system security. We formulate the problem as a Markov decision process with a temporally joint chance constraint that forces the adversary to avoid being detected throughout the considered time period. Owing to the joint constraint, the optimal control policy depends not only on the current state but also on the entire history, which leads to the explosion of the search space and makes the problem generally intractable. It is shown that whether an alarm has been triggered or not, in addition to the current state is sufficient for specifying the optimal decision at each time step. Augmentation of the information to the state space induces an equivalent convex optimization problem, which is tractable using standard solvers.
