Towards Secure and Resilient Cyber-Physical Systems

迈向安全且有弹性的网络物理系统

基本信息

  • 批准号:
    RGPIN-2021-04342
  • 负责人:
  • 金额:
    $ 1.75万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Discovery Grants Program - Individual
  • 财政年份:
    2022
  • 资助国家:
    加拿大
  • 起止时间:
    2022-01-01 至 2023-12-31
  • 项目状态:
    已结题

项目摘要

Cyber Physical Systems (CPSs) are the future of critical infrastructure such as transportation systems, electric grids, medical support, and major smart city applications driven by the Internet of Things (IoT). CPSs are interconnected, software-dependent systems with embedded sensors and actuators performing unconventional, complex interactions. Hence, they generally introduce an unexplored territory of security vulnerabilities and entail wider attack surfaces due to the physical processes involved and the deeply integrated cyber-physical space. Many CPS applications analyze the data collected from the physical world to generate critical decisions in real time. Consequently, erroneous or suboptimal decisions due to cyberattacks could have a disastrous impact on the CPS operations, its dependability, and safety. Furthermore, the prevention and detection of increasingly evolving cyber-physical attacks are not always successful. Therefore, CPSs should be designed with built-in tolerance to intrusions to guarantee an acceptable level of reliable performance under potential compromise. This research aims to design secure and resilient CPSs against stealthy attacks that target the perception, network, and application layers. It will address the three fundamental facets of computer and information security (i.e., intrusion prevention, detection, and mitigation) while respecting the intrinsic features (e.g., resource-constrained) and functional requirements (e.g., large scale) of CPSs. The proposed program is highly multidisciplinary in nature and encompasses three main tracks. The first track consists in developing smart intrusion prevention techniques as the primary line of defence, with special focus on the design of efficient authentication and authorization schemes, tailored to specific CPS environments such as connected vehicles and IoT. The second track is directed towards building robust, context-aware intrusion detection systems in highly dynamic CPSs by leveraging the power of deep learning techniques to enable more accurate estimation of abnormal system states and the detection of zero-day attacks. The third track involves the design of a new fabric of multi-layer, adaptive resilience which deeply integrates a wide range of advanced attack mitigation solutions, intelligent node collaboration approaches, and optimized incident recovery measures into the CPS operations and decision making at runtime. Together, the three tracks will create an original, defense-in-depth security strategy, with predominant focus on the initial design phases of secure and dependable cyber-physical software. The strategy will yield tremendous impact on current CPS design and implementation choices in the Canadian industry as well as nationwide critical infrastructures. The outcomes of the program will equip the research community with innovative security mindsets and tools that are essential to the radical advancement of the security status of emerging CPSs.
网络物理系统(CPS)是关键基础设施的未来,如交通系统,电网,医疗支持和物联网(IoT)驱动的主要智慧城市应用。CPS是相互连接的软件依赖系统,具有执行非常规复杂交互的嵌入式传感器和执行器。因此,它们通常会引入一个未经探索的安全漏洞领域,并由于所涉及的物理过程和深度集成的网络物理空间而带来更广泛的攻击面。许多CPS应用程序分析从物理世界收集的数据,以真实的时间生成关键决策。因此,由于网络攻击而导致的错误或次优决策可能会对CPS运营、其可靠性和安全性产生灾难性影响。此外,预防和检测日益演变的网络物理攻击并不总是成功的。因此,CPS的设计应该具有内置的入侵容忍能力,以保证在潜在的危害下具有可接受的可靠性能水平。本研究的目的是设计安全和弹性的CPS对隐形攻击的目标感知,网络和应用层。它将解决计算机和信息安全的三个基本方面(即,入侵预防、检测和缓解)同时尊重固有特征(例如,资源受限的)和功能需求(例如,大规模的CPS。拟议的方案是高度多学科的性质,包括三个主要轨道。第一条轨道包括开发智能入侵防御技术作为主要防线,特别关注设计高效的身份验证和授权方案,为特定的CPS环境(如联网车辆和物联网)量身定制。第二个方向是在高度动态的CPS中构建强大的上下文感知入侵检测系统,通过利用深度学习技术的力量来更准确地估计异常系统状态和检测零日攻击。第三条轨道涉及设计一种多层自适应弹性的新结构,该结构将广泛的高级攻击缓解解决方案,智能节点协作方法和优化的事件恢复措施深度集成到CPS运营和运行时决策中。这三条轨道将共同创建一个原创的深度防御安全战略,主要关注安全可靠的网络物理软件的初始设计阶段。该战略将产生巨大的影响,目前CPS的设计和实施选择在加拿大行业以及全国范围内的关键基础设施。该计划的成果将为研究界提供创新的安全思维方式和工具,这些思维方式和工具对新兴CPS的安全状态的根本进步至关重要。

项目成果

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Halabi, Talal其他文献

A deep learning approach for proactive multi-cloud cooperative intrusion detection system
Protecting the Internet of Vehicles Against Advanced Persistent Threats: A Bayesian Stackelberg Game
  • DOI:
    10.1109/tr.2020.3046688
  • 发表时间:
    2021-09-01
  • 期刊:
  • 影响因子:
    5.9
  • 作者:
    Halabi, Talal;Wahab, Omar Abdel;Zulkernine, Mohammad
  • 通讯作者:
    Zulkernine, Mohammad

Halabi, Talal的其他文献

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{{ truncateString('Halabi, Talal', 18)}}的其他基金

Towards Secure and Resilient Cyber-Physical Systems
迈向安全且有弹性的网络物理系统
  • 批准号:
    DGECR-2021-00425
  • 财政年份:
    2021
  • 资助金额:
    $ 1.75万
  • 项目类别:
    Discovery Launch Supplement
Towards Secure and Resilient Cyber-Physical Systems
迈向安全且有弹性的网络物理系统
  • 批准号:
    RGPIN-2021-04342
  • 财政年份:
    2021
  • 资助金额:
    $ 1.75万
  • 项目类别:
    Discovery Grants Program - Individual

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