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相关概念视频

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确保云辅助连接和自动驾驶汽车的安全:深入的威胁分析和风险评估.

Al Tariq Sheik1, Carsten Maple1, Gregory Epiphaniou1

  • 1Warwick Manufacturing Group (WMG), University of Warwick, Coventry CV4 7AL, UK.

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概括
此摘要是机器生成的。

这项研究增强了云辅助连接和自动驾驶汽车 (CCAV) 的威胁分析和风险评估 (TARA). 它完善了TARA的方法,以更好地识别复杂的CCAV系统中的漏洞和管理风险.

关键词:
攻击树攻击树自动驾驶汽车是自动驾驶的这是一个云云云.连接的车辆连接的车辆.采取反制措施的措施.网络安全 网络安全边缘计算是一种边缘计算.风险评估 风险评估 风险评估分类学 分类学.威胁分析,威胁分析.威胁建模威胁建模

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科学领域:

  • 网络安全 网络安全
  • 汽车工程 汽车工程
  • 风险管理 风险管理

背景情况:

  • 云辅助连接和自动驾驶汽车 (CCAV) 面临越来越多的网络攻击风险,因为威胁和复杂的系统不断发展.
  • 现有的威胁分析和风险评估 (TARA) 方法在捕获CCAV特定威胁数据方面往往不足,导致效率下降.
  • 诸如复杂的硬件-软件交互,快速的技术变化和多样化的标准等因素使CCAV安全性复杂化.

研究的目的:

  • 系统地评估和增强TARA对CCAVs的方法.
  • 在CCAV系统中提高识别漏洞,量化风险和定义威胁边界的精度.
  • 开发一种新的防御分类学,并为增强安全工程提供先进的威胁分析信息.

主要方法:

  • 对现有的TARA方法进行系统评估.
  • 应用伪造,改,拒绝,信息披露,拒绝服务和特权升级 (STRIDE) 威胁模型.
  • 利用损害,可重复性,可利用性,受影响的用户和可发现性 (DREAD) 进行风险评估.
  • 开发一个攻击树和一个新的防御分类学.

主要成果:

  • 在CCAV架构中确定了特定的漏洞和量化了相关风险.
  • 证明了改进后的TARA在更精确地捕获安全要求,威胁和限制方面的更强效.
  • 提供了一个结构化的方法来理解和减轻对硬件-软件资产的多阶段攻击.

结论:

  • 拟议的TARA增强方案为CCAV提供了更精确,更有效的网络安全方法.
  • 解决复杂的交互和新出现的漏洞对于强大的CCAV安全至关重要.
  • 这项研究为网络物理CCAV系统中先进的威胁分析和风险管理提供了基础.