Emerging methods in critical infrastructure security and resilience

随着信息和计算系统的规模和复杂性的不断增长，安全已成为每一种网络技术设计的基石之一。攻击者可能使用人工智能来对抗基于人工智能的网络防御是一个问题，它代表了一个需要调查的关于基于人工智能的防御者和基于人工智能的攻击者之间军备竞赛的可能性的基本问题。这场军备竞赛可能会向有利于一方或另一方的方向发展，或者相反，创造不稳定的条件，这本身就构成了对网络安全和我们社会的新威胁。在过去，我们已经开发并发布了高度智能化的解决方案，用于在关键基础设施上运行的计算机网络的规划、设计和运行。由于其特殊性，在确保数据保护和隐私的同时，提高新兴网络系统的安全性和弹性要求在工程上进行高水平的创新。本研究的目的是提供创新的解决方案，以保护新兴的技术意识关键基础设施。我们计划通过开发新的基于人工智能的工具和方法来加强监控，这些工具和方法将自动化一些检测、关联和分析功能，同时确保数据保护和隐私。我们还致力于建模复杂的基于人工智能的网络攻击，目标是基础设施，以破坏控制纪律。通过暴露漏洞并了解攻击，我们计划设计一个流程，以有效地创建一类新的特定于领域的防御安全性和隐私解决方案。最后，这将使我们能够探索网络安全解决方案的弹性，试图模拟和理解关键基础设施网络安全中潜在的人工智能对抗人工智能军备竞赛。由于保护在关键基础设施上运行的大规模系统的复杂性，大数据分析、机器学习和分析工具开辟了防止网络攻击的新途径。我们将尝试使用深度学习神经网络和强化学习技术来实现复杂的基于人工智能的攻击和防御。我们还将尝试联邦学习，特别是联邦人工智能及其架构，以解决数据访问挑战。我们将使用区块链技术来应对网络安全挑战，这些挑战需要一个分布式、安全、隐私保护和不可变的记录保存框架，以在信息共享期间实现信任。运行我们关键基础设施的网络物理系统，以及无数加拿大企业所依赖的网络物理系统，很容易受到网络罪犯的攻击。本研究的创新旨在开发新方法，以应对网络安全威胁对加拿大经济和社会构成的威胁。我们的研究有能力导致人工智能共同进化的理论，这将使我们能够防止不稳定和冲突的情况。

With the growing scale and complexity of information and computing systems, security has become one of the cornerstones in the design of every cyber technology. The potential use of Artificial Intelligence (AI) by attackers to counter AI-based cyber-defense is a problem and represents a fundamental question regarding the possibility of an arms race between AI-based defenders and AI-based attackers that needs to be investigated. This arms race could converge in favor of one or the other, or on the contrary create unstable conditions that constitutes in itself a new threat to cybersecurity and our society. In the past, we have developed and published highly intelligent solutions for the planning, design and operation of computer networks running on critical infrastructures. Because of their special nature, improving the security and resilience of emerging cyber systems while ensuring data protection and privacy mandates a high level of innovation in engineering. The purpose of this research is to provide innovative solutions to protect emerging technology-aware critical infrastructures. We plan on enhancing monitoring by developing novel AI-based tools and methods that will automate some of the detection, correlation, and analysis functions while ensuring data protection and privacy. We also aim at modeling sophisticated AI-based cyberattacks targeting the infrastructure to disrupt the control discipline. By exposing the vulnerabilities and understanding the attacks, we plan on designing a process for effectively creating a new class of domain-specific defensive security and privacy solutions. Finally, this will enable us to explore the resilience of the cybersecurity solutions in an attempt to model and understand the potential AI against AI arms race in cybersecurity of critical infrastructures. Due to the complexity of securing large scale systems running on critical infrastructures, big data analysis, machine learning and analytical tools open up new avenues of protection against cyber attacks. We will experiment with deep learning neural networks and reinforcement learning techniques to implement sophisticated AI-based attacks and defenses. We will also experiment with federated learning particularly federated AI and its architectures to address data access challenges. We will use the blockchain technology to address cybersecurity challenges that require a distributed, secure, privacy-preserving, and immutable record-keeping framework to enable trust during information sharing. The cyber physical systems running our critical infrastructures, and on which countless Canadian businesses depend, are vulnerable to attacks by cyber criminals. The innovation of this research aims at developing novel approaches to counter the threat to our Canadian economy and society posed by cybersecurity threats. Our research has the capacity to lead to a theory of AI co-evolution that will allow us to prevent conditions of instability and conflict.