Abstract
Networked-Control Systems (NCSs), a type of cyber-physical systems, consist of tightly integrated computing, communication and control technologies. While being very flexible environments, they are vulnerable to computing and networking attacks. Recent NCSs hacking incidents had major impact. They call for more research on cyber-physical security. Fears about the use of quantum computing to break current cryptosystems make matters worse. While the quantum threat motivated the creation of new disciplines to handle the issue, such as post-quantum cryptography, other fields have overlooked the existence of quantum-enabled adversaries. This is the case of cyber-physical defense research, a distinct but complementary discipline to cyber-physical protection. Cyber-physical defense refers to the capability to detect and react in response to cyber-physical attacks. Concretely, it involves the integration of mechanisms to identify adverse events and prepare response plans, during and after incidents occur. In this paper, we assume that the eventually available quantum computer will provide an advantage to adversaries against defenders, unless they also adopt this technology. We envision the necessity for a paradigm shift, where an increase of adversarial resources because of quantum supremacy does not translate into a higher likelihood of disruptions. Consistently with current system design practices in other areas, such as the use of artificial intelligence for the reinforcement of attack detection tools, we outline a vision for next generation cyber-physical defense layers leveraging ideas from quantum computing and machine learning. Through an example, we show that defenders of NCSs can learn and improve their strategies to anticipate and recover from attacks.
Highlights
Networked-Control Systems (NCSs), a type of cyber-physical systems, consist of tightly integrated computing, communication and control technologies
In a NetworkedControl System (NCS), the focus is on remote control, which means steering at distance a dynamical system according to requirements
We show that a defender can leverage quantum machine learning to address the quantum challenge
Summary
Protection is one of the most important branches of cybersecurity. It mainly relies on the implementation of state-of-the-art cryptographic protocols. As a function of p and q, on which the agent has no control, the learned policy is that in state zero should pick the action among a0 and a1 that fields the maximum Q-value, which can be determined from Fig. 5 This figure highlights the usefulness of RL, even for such a simple example the exact action choice is by far not always obvious. Part (d) shows the evolution of the probabilities of the actions, as the training of the quantum variational circuit pictured in Fig. 6 progresses. They evolve consistently with the value of state zero (learning rate α is 0.01). It has higher probability than the take bypass action
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