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
摘要：
我们研究了线性系统中基于学习的攻击问题，其中控制器和被控对象之间的通信信道可能被恶意攻击者劫持。我们假设攻击者从观察中学习系统的动态，然后覆盖控制器的致动信号，同时通过向控制器提供虚构的传感器读数来模仿合法操作。另一方面，控制器是一个瞭望台，以检测攻击者的存在，并试图通过仔细制作其控制信号，以提高检测性能。我们研究了攻击者从观测中获得的信息，控制器的检测能力和控制成本之间的权衡。具体而言，我们提供严格的上限和下限上的预期$ε$欺骗时间，即所需的时间由控制器作出决定，关于攻击者的存在与信心至少$(1-ε\log(1/ε))$。然后，我们展示了一个概率下界的时间，必须花费的攻击者学习系统，为了使控制器有一个给定的预期$ε$欺骗时间。我们证明了这个界也是阶最优的，从这个意义上说，如果攻击者满足它，那么存在具有给定阶期望欺骗时间的学习算法。最后，我们显示了一个下界的预期能量消耗，以保证检测的信心至少$1-ε\log(1/ε)$。
英文摘要：
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 $1-ε\log(1/ε)$.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
