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1Department of Computer Technology, Madras Institute of Technology, Anna University, Chennai, India. anandmunuswamy90@gmail.com.
Scientific reports
|July 24, 2025
概括
本研究介绍了用于车辆互联网的改进入侵检测系统 (IDS). 新系统提高了攻击检测的准确性,并减少了智能运输系统中的假阳性.
科学领域:
- 网络物理系统 网络物理系统
- 智能运输系统 智能运输系统
- 网络安全 网络安全
背景情况:
- 车辆互联网 (IoV) 对智能交通系统至关重要,但面临着越来越多的网络物理攻击.
- 现有的入侵检测系统 (IDS) 往往缺乏足够的准确性,并且具有很高的假阳性率.
- 需要有效的安全解决方案来缓解车辆网络中的网络威胁.
研究的目的:
- 提出一种高效的特征选择算法和一种新的分类算法,用于在 IoV 中增强入侵检测.
- 为了提高攻击识别的准确性,同时最大限度地减少车辆网络物理系统中的错误阳性.
- 解决现有的IDS在检测复杂的网络物理攻击方面的局限性.
主要方法:
- 开发了一种基于权重和排名的特征选择算法,用于高效的特征选择.
- 提出了一个基于包装的迷糊卷积神经网络 (FCNN) 分类算法与亚当优化器.
- 在深度卷积神经网络分类器中集成的模糊推断,以改进攻击识别.
主要成果:
- 与现有方法相比,拟议的IDS显示了更高的检测准确性.
- 观察到虚假阳性率的显著降低.
- 该系统在基准和网络跟踪数据集上都被证明是有效的.
结论:
- 拟议的基于权重和排名的特征选择算法和基于包装的FCNN为IoV安全提供了更有效的解决方案.
- 开发的IDS通过提高准确性和减少假阳性来显著提高入侵检测性能.
- 这项研究有助于加强智能运输系统对抗网络物理攻击的安全性.
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