In cyber-physical systems (CPSs), it is typical that a sensor observes a dynamical process and transmits the state estimate to a remote estimator wirelessly. Security risks arise when a denial-of-service (DoS) attacker generates extra noise at some power level to reduce the successful transmission rate. Investigating the capability of such an attacker to endanger the system is an important research line in CPS security. However, most previous works have two restrictions, one is that the attacker has complete knowledge of the system, which is usually difficult to achieve, and the other is that the attack power level set is small and discrete, which reduces the attack effectiveness and is hard to be implemented in multi-process systems due to the curse of dimensionality. In this paper, we tackle these restrictions by establishing a continuous attack power design for a DoS attacker with limited information. We propose deep deterministic policy gradient (DDPG)-based attack designs in single-process and multi-process systems, respectively. Numerical simulations illustrate the advantages of DDPG-based attack designs over heuristic baselines and existing learning methods.

中文翻译：

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针对无线网络上的网络物理系统的连续空间中的战略性 DoS 攻击

在信息物理系统 (CPS) 中，传感器通常会观察动态过程并将状态估计无线传输到远程估计器。当拒绝服务 (DoS) 攻击者在某些功率级别产生额外噪声以降低成功传输率时，就会出现安全风险。调查此类攻击者危害系统的能力是 CPS 安全的重要研究方向。但是，以往的大部分工作都有两个限制，一是攻击者对系统有完整的了解，这通常很难做到，二是攻击力水平集小且离散，降低了攻击有效性，并且是由于维度灾难，很难在多进程系统中实现。在本文中，我们通过为信息有限的 DoS 攻击者建立持续的攻击能力设计来解决这些限制。我们分别在单进程和多进程系统中提出了基于深度确定性策略梯度 (DDPG) 的攻击设计。数值模拟说明了基于 DDPG 的攻击设计相对于启发式基线和现有学习方法的优势。