标题： 基于学习的面向恶意攻击的并行服务器系统成本感知防御 显示英文标题

标题： Learning-Based Cost-Aware Defense of Parallel Server Systems against Malicious Attacks

摘要： 我们考虑并行服务器系统的网络物理安全，这与网络、制造和运输等多种工程应用相关。 这些系统依赖于反馈控制，因此可能容易受到拒绝服务、数据伪造和指令篡改等恶意攻击。 在本文中，我们开发了一种学习算法，该算法计算一种防御策略，以平衡防御行动的技术成本和上述网络攻击导致的性能下降。 我们考虑一个零和马尔可夫安全博弈。 我们开发了一种近似最小最大-Q学习算法，该算法可以高效地计算博弈的均衡，从而得到一种成本感知的防御策略。 该算法使用针对系统结构的可解释线性函数逼近。 我们证明，在温和假设下，该算法以概率1收敛到近似的马尔可夫完美均衡。 我们首先使用李雅普诺夫方法来处理由于无限状态空间导致的无界时间差误差。 然后我们使用基于常微分方程的论证来建立收敛性。 仿真结果表明，我们的算法收敛速度比一种典型的基于神经网络的方法快约50倍，最优性差距在4%--8%之间，具体取决于线性逼近器的复杂性和并行服务器的数量。 显示英文摘要

摘要： We consider the cyber-physical security of parallel server systems, which is relevant for a variety of engineering applications such as networking, manufacturing, and transportation. These systems rely on feedback control and may thus be vulnerable to malicious attacks such as denial-of-service, data falsification, and instruction manipulations. In this paper, we develop a learning algorithm that computes a defensive strategy to balance technological cost for defensive actions and performance degradation due to cyber attacks as mentioned above. We consider a zero-sum Markov security game. We develop an approximate minimax-Q learning algorithm that efficiently computes the equilibrium of the game, and thus a cost-aware defensive strategy. The algorithm uses interpretable linear function approximation tailored to the system structure. We show that, under mild assumptions, the algorithm converges with probability one to an approximate Markov perfect equilibrium. We first use a Lyapunov method to address the unbounded temporal-difference error due to the unbounded state space. We then use an ordinary differential equation-based argument to establish convergence. Simulation results demonstrate that our algorithm converges about 50 times faster than a representative neural network-based method, with an insignificant optimality gap between 4\%--8\%, depending on the complexity of the linear approximator and the number of parallel servers.