论文标题
网络物理系统中基于学习的攻击：探索，检测和控制成本权衡
Learning-based attacks in Cyber-Physical Systems: Exploration, Detection, and Control Cost trade-offs
论文作者
论文摘要
我们研究了线性系统中基于学习的攻击问题，在该系统中，控制器和工厂之间的通信渠道可以被恶意攻击者劫持。我们假设攻击者从观察值中学习了系统的动态，然后覆盖了控制器的驱动信号，同时通过向控制器提供虚拟的传感器读数来模仿合法操作。另一方面，控制器正在监视器中检测攻击者的存在，并试图通过仔细制定其控制信号来增强检测性能。我们研究攻击者从观察结果，控制器的检测能力和控制成本中获得的信息之间的权衡。具体而言，我们在预期的$ε$纳税时间上提供了紧密的上限和下限，即控制器对有信心至少$（1-ε\ log（1/ε））$的攻击者做出决定所需的时间。然后，我们在攻击者学习系统必须花费的时间上显示一个概率的下限，以便控制器具有给定的预期$ε$ - 诱导时间。我们表明，从某种意义上说，如果攻击者满足它，则该界限也是最佳的，那么就会存在一种学习算法，其中给定的顺序预期的欺骗时间。最后，我们显示了以置信度保证至少$ 1-ε\ log（1/ε）$确保检测所需的预期能量消耗的下限。
We study the problem of learning-based attacks in linear systems, where the communication channel between the controller and the plant can be hijacked by a malicious attacker. We assume the attacker learns the dynamics of the system from observations, then overrides the controller's actuation signal, while mimicking legitimate operation by providing fictitious sensor readings to the controller. On the other hand, the controller is on a lookout to detect the presence of the attacker and tries to enhance the detection performance by carefully crafting its control signals. We study the trade-offs between the information acquired by the attacker from observations, the detection capabilities of the controller, and the control cost. Specifically, we provide tight upper and lower bounds on the expected $ε$-deception time, namely the time required by the controller to make a decision regarding the presence of an attacker with confidence at least $(1-ε\log(1/ε))$. We then show a probabilistic lower bound on the time that must be spent by the attacker learning the system, in order for the controller to have a given expected $ε$-deception time. We show that this bound is also order optimal, in the sense that if the attacker satisfies it, then there exists a learning algorithm with the given order expected deception time. Finally, we show a lower bound on the expected energy expenditure required to guarantee detection with confidence at least $1-ε\log(1/ε)$.
