在车载通信互联网中进行攻击检测的元启发性优化复杂值扩展循环神经网络
Prasanalakshmi Balaji1, Korhan Cengiz2,3, Sangita Babu4
1Department of Computer Science, King Khalid University, Alqaraa, Saudi Arabia.
PeerJ. Computer science
|December 9, 2024
概括
本研究引入了一种新的深度学习模型,用于检测汽车互联网 (IoV) 中的攻击. 先进的复杂值扩展循环神经网络 (CV-DRNN) 提高了车辆网络的安全性.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 运输系统 运输系统
背景情况:
- 车辆互联网 (IoV) 通过实时数据交换增强了运输,但面临着重大安全挑战.
- 现有的攻击检测方法可能是昂贵和复杂的,限制了它们在资源有限的环境中部署.
研究的目的:
- 为 IoV 网络开发一种创新高效的攻击检测模型.
- 提高车辆通信系统的安全性和可靠性.
主要方法:
- 使用深度学习技术,特别是复杂值扩展循环神经网络 (CV-DRNN).
- 采用增强利用在混合型基于Leader的优化 (EEHLO) 方法,从收集的数据中进行最佳的特征提取.
- 从在线数据库收集数据,用于模型培训和评估.
主要成果:
- 拟议的CV-DRNN模型在车辆网络中展示了准确的攻击检测能力.
- 新型模型的性能被严格评估,并与传统的攻击检测方法进行了比较.
结论:
- 开发的深度学习模型为增强IoV安全提供了有效的解决方案.
- 这种方法解决了当前方法的局限性,为更安全的车辆网络铺平了道路.
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