使用基于层的范式和物联网的恶意车辆检测.
Abdul Razaque1, Gulnara Bektemyssova2, Joon Yoo1
1School of Computing, Gachon University, Seongnam-si 13120, Republic of Korea.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究介绍了一个集团区块链启用常规神经网络 (CBCNN) 来检测恶意车辆. 该CBCNN模型提高了安全性,并降低了车辆互联网 (IoV) 的成本.
科学领域:
- 人工智能和机器学习
- 交通运输中的网络安全
- 区块链技术应用 区块链技术应用
背景情况:
- 深度学习模型在许多领域都至关重要,但容易受到恶意攻击.
- 保护人工智能模型,特别是在车辆检测等关键应用中,是必不可少的.
- 现有的方法可能缺乏强大的安全性,防止车辆网络中的参数操纵.
研究的目的:
- 为了提出一个新的四层模式,联盟启用了区块链常规神经网络 (CBCNN),用于检测恶意车辆.
- 确保在车辆互联网 (IoVs) 中防止参数操纵攻击的防改保护.
- 通过多标签分类和区块链集成,提高车辆管理系统的安全性和可靠性.
主要方法:
- 开发了一个四层CBCNN模型,包括卷积神经网络 (CNN) -IoT,空间金字塔聚合,完全连接的层和联盟区块链.
- 在联盟区块链中实施了一种运气证明机制,以优化车辆的能源消耗.
- 使用C++实现和ns-3.34平台与ns3-ai模块用于模拟和异常检测在IoVs.
主要成果:
- 与最先进的方法相比,CBCNN方法在恶意标签检测方面表现优越.
- 在识别恶意车辆方面实现了更高的平均准确性和更低的损失比率.
- 展示了显著的成本降低效益,同时确保人工智能模型的防改保护.
结论:
- 拟议的CBCNN模型有效地识别恶意载体,并减轻IOV中相关的风险.
- 联盟区块链集成为车辆系统中的深度学习模型提供了强大的,防改的安全性.
- CBCNN的方法提供了一种节能且具有成本效益的解决方案,以确保车辆互联网的安全.
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