一个智能动态网络物理系统威胁检测系统,用于确保6G自动驾驶汽车网络的安全通信
Shanthalakshmi M1, Ponmagal R S2
1Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, Tamil Nadu, India. mshanthi.s@gmail.com.
Scientific reports
|September 6, 2024
概括
本研究介绍了使用卷积神经网络 (CNN) 的自动驾驶汽车 (AV) 智能入侵检测系统. 该系统有效检测网络攻击,增强智能城市交通系统的安全性.
科学领域:
- 网络物理系统 (CPS)
- 智能运输系统 (ITS) 是一种智能运输系统.
背景情况:
- 自动驾驶汽车 (AV) 是智能城市发展的组成部分,代表先进的网络物理系统 (CPS).
- 6G技术与自动驾驶汽车的整合有望增强智能运输系统 (ITS),但引入了重要的网络安全漏洞.
- 对自动驾驶汽车的网络攻击可能会危及安全,破坏运输网络,并造成经济损失.
研究的目的:
- 在CPS框架内开发一种新的方法,用于在AV中动态检测网络攻击.
- 提高自动驾驶汽车网络对网络威胁的安全性和可靠性.
- 将网络安全模型与智能城市集成的新兴6G标准保持一致.
主要方法:
- 利用预训练的卷积神经网络 (CNN) 模型来检测网络攻击.
- 实施智能入侵检测系统 (IIDS),使用数据融合,一类支持向量机,随机森林和k-最近邻近算法.
- 评估各种机器学习技术用于入侵检测的性能.
主要成果:
- 拟议的IIDS在检测AV CPS中的网络攻击方面表现出高准确性.
- EfficientNet模型实现了高达99.97%的卓越检测准确度.
- 该研究验证了高级学习技术在缓解安全风险方面的有效性.
结论:
- 开发的IIDS显著提高了自动驾驶汽车网络的网络安全.
- 这项研究有助于创建强大的6G智能网络安全模型.
- 这项工作支持将自动驾驶汽车安全有效地整合到未来的智能城市.
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