支持6G的基于量子位的隐蔽通信,在自动驾驶车队中使用人工智能驱动的漏洞检测.
Venkatesh T1, SatheeshKumar Palanisamy2, Maha Abdelhaq3
1Department of ECE, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu, India.
Scientific reports
|December 5, 2025
概括
本研究介绍了一种用于自动驾驶汽车的隐藏导航通信安全模块 (SM-CNC). 该模块通过使用量子计算和循环学习来增强安全性,提高身份验证率并减少通信故障.
科学领域:
- 网络安全 网络安全
- 量子计算是一种量子计算.
- 人工智能的人工智能
背景情况:
- 自动驾驶汽车 (AV) 需要安全的通信道来进行导航和数据交换.
- 当前的通信方法面临着复杂的计算和异质环境中的潜在安全漏洞的挑战.
- 安全的信息交换对于自动驾驶设备至关重要,以确保精确的导航,物体检测和可靠的数据传输.
研究的目的:
- 为自动驾驶汽车环境设计了一种新的隐藏导航通信安全模块 (SM-CNC).
- 通过解决复杂的计算和拦截风险,提高自动驾驶汽车通信的安全性和可靠性.
- 利用量子计算和循环学习来实现安全和高效的信息交换.
主要方法:
- 拟议的SM-CNC模块使用了在拦截时解体的挥发性身份验证.
- 如果未经授权的车辆拦截,信息会被丢弃,在传输和验证过程中进行监控.
- 该模块采用量子计算,将信息点建模为量子比特,并使用循环学习来实现安全和可靠的通信.
主要成果:
- 在SM-CNC模块中,认证率提高了9.29%.
- 通信故障减少了11.87%.
- 计算复杂性减少了12.13%,以实现每个间隔的最大通信请求.
结论:
- 拟议的SM-CNC模块显著提高了自动驾驶汽车的通信安全性和可靠性.
- 量子计算和循环学习的整合提供了针对安全漏洞和未经授权访问的强有力的解决方案.
- SM-CNC提供了改进的性能指标,使AV通信更加安全和高效.
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