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    陈欣, 程吉祥, 姜孪娟. 网络物理融合下基于随机博弈的车联网网络安全可靠性分析[J]. 应用基础与工程科学学报, 2024, 32(3): 685-701. DOI: 10.16058/j.issn.1005-0930.2024.03.005
    引用本文: 陈欣, 程吉祥, 姜孪娟. 网络物理融合下基于随机博弈的车联网网络安全可靠性分析[J]. 应用基础与工程科学学报, 2024, 32(3): 685-701. DOI: 10.16058/j.issn.1005-0930.2024.03.005
    CHEN Xin, CHENG Jixiang, JIANG Luanjuan. Reliability Analysis of the Internet of Vehicles Cybersecurity Based on Stochastic Game Under Cyber-Physical Integration[J]. Journal of Basic Science and Engineering, 2024, 32(3): 685-701. DOI: 10.16058/j.issn.1005-0930.2024.03.005
    Citation: CHEN Xin, CHENG Jixiang, JIANG Luanjuan. Reliability Analysis of the Internet of Vehicles Cybersecurity Based on Stochastic Game Under Cyber-Physical Integration[J]. Journal of Basic Science and Engineering, 2024, 32(3): 685-701. DOI: 10.16058/j.issn.1005-0930.2024.03.005

    网络物理融合下基于随机博弈的车联网网络安全可靠性分析

    Reliability Analysis of the Internet of Vehicles Cybersecurity Based on Stochastic Game Under Cyber-Physical Integration

    • 摘要: 交通事故已经成为威胁人民生命和财产安全的社会公害之一,车联网(Internet of Vehicles,IoV)作为能够有效增加道路安全的新兴技术,在道路交通安全防控方面扮演着至关重要的角色,在广泛应用的同时也面临着严峻的网络攻击挑战.为分析IoV系统可靠性对道路安全水平的影响,设计了基于马尔可夫过程的双人随机博弈模型,将受到攻击的IoV系统分为渗透阶段、破坏阶段与主动恢复阶段,基于IoV系统受攻击后的状态转移过程,研究了攻击者和防御者之间动态交互的影响及状态之间的级联关系.将IoV系统的安全部署成本划分为相互依赖的网络层和物理层两部分,在无法完全了解模型参数的情况下通过Minimax-Q算法预测攻击者行为,探究防御成功率与网络安全部署成本对攻击者策略的影响,评价了不同攻击策略下的系统可靠性(车辆系统平均失败时间、车辆系统稳态可用性和道路信息稳态机密性),并构建了车联网环境下的道路交通安全综合指标,探析了IoV系统网络安全对道路交通安全水平的影响.研究结果表明:(1)在渗透阶段,相比于网络层,物理层安全部署成本的变化对攻击策略的影响更为显著,在破坏阶段则呈相反趋势.(2)在应用受损状态下,车辆系统稳态可用性,道路信息稳态机密性和道路交通安全综合水平分别上升了3.9%、3.9%和1.34%.(3)渗透阶段(N,V,P)为IoV系统可靠性对道路交通安全水平影响方面的关键阶段,极大程度上威胁着道路交通安全.以期该研究成果有助于确定IoV系统网络安全的关键阶段,并增强道路交通安全水平.

       

      Abstract: Traffic accidents have become a pressing societal issue,endangering lives and property.The Internet of Vehicles (IoV) technology has the potential to significantly improve road safety.This paper proposes a novel two-player stochastic game model,grounded in Markov decision processes,to aid IoV system operators in understanding the dynamic interactions between attackers and defenders,as well as the cascading effects between different system states.The attacked IoV system is divided into three phases:penetration,disruption,and proactive recovery.The deployment cost of IoV system is divided into intertwined cyber and physical layers.By refining the cost settings for both attackers and defenders,Minimax-Q is employed to predict attacker actions,even with incomplete knowledge of model parameters.The impact of defense effectiveness and cybersecurity deployment cost on attacker strategies is explored,assessing the system reliability under various attack strategies.Key metrics include Vehicle System Mean Time to Failure (MTTF),Vehicle System Steady-State Availability (SSA),and Road Information Steady-State Confidentiality (SSC).A novel composite index,the Road Safety and Traffic Integrity Indicator (RTSL),is formulated to assess how IoV system cybersecurity reliability influences road traffic safety.Our findings indicate:(1)In the penetration phase,fluctuations in the deployment cost of the physical layer’s security have a more significant influence on attack strategies compared to the cyber layer,while the trend reverses in the disruption phase.(2)Under a specific compromised scenario A,the IoV system’s robust adaptability enables rapid detection and mitigation of increased attack likelihood,leading to improvements of 3.9% in SSA,3.9% in SSC,and 1.34% in RTSL.(3) The penetration phase (N,V,P) is identified as a crucial period affecting the IoV system’s impact on road traffic safety,posing a significant threat to road traffic safety.These insights assist IoV system operators in making informed cybersecurity resource allocations,improving cybersecurity in critical states,and ultimately the overall road traffic safety level.

       

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