Manipulating a Learning Defender and Ways to Counteract
Manipulating a Learning Defender and Ways to Counteract
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发表时间：
2019-05
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通讯作者：
Jiarui Gan;Qingyu Guo;Long Tran-Thanh;Bo An;M. Wooldridge
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作者：
Jiarui Gan;Qingyu Guo;Long Tran-Thanh;Bo An;M. Wooldridge
在Stackelberg安全游戏中，当攻击者的收益信息不确定时，已经提出了通过与攻击者交互并观察他们的最佳响应来学习最佳防御者承诺的算法。在本文中，我们表明，但是，这些算法可以很容易地操纵，如果攻击者不如实地响应。作为一个关键发现，攻击者的操纵通常会导致防御者学习最大最小策略，这实际上使学习尝试变得毫无意义，因为计算最大最小策略根本不需要其他参与者的额外信息。然后，我们在更高的层次上应用博弈论框架来对抗这种操纵，其中防御者承诺根据所学到的信息来指定她的策略承诺的政策。我们提供了一个多项式时间算法来计算最优的这样的政策，此外，一个启发式的方法，即使当攻击者的回报空间是无限的或完全未知的。实证评估表明，我们的方法可以提高防御者的效用显着相比，当攻击者操纵被忽略的情况。
In Stackelberg security games when information about the attacker's payoffs is uncertain, algorithms have been proposed to learn the optimal defender commitment by interacting with the attacker and observing their best responses. In this paper, we show that, however, these algorithms can be easily manipulated if the attacker responds untruthfully. As a key finding, attacker manipulation normally leads to the defender learning a maximin strategy, which effectively renders the learning attempt meaningless as to compute a maximin strategy requires no additional information about the other player at all. We then apply a game-theoretic framework at a higher level to counteract such manipulation, in which the defender commits to a policy that specifies her strategy commitment according to the learned information. We provide a polynomial-time algorithm to compute the optimal such policy, and in addition, a heuristic approach that applies even when the attacker's payoff space is infinite or completely unknown. Empirical evaluation shows that our approaches can improve the defender's utility significantly as compared to the situation when attacker manipulation is ignored.