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相关概念视频

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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相关实验视频

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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使用级联深度学习方法进行车辆网络入侵检测,并使用多变量元启发式的多变量元启发式方法.

Ankit Manderna1, Sushil Kumar1, Upasana Dohare2

  • 1School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India.

Sensors (Basel, Switzerland)
|November 14, 2023
PubMed
概括

本研究介绍了一种人工智能驱动的网络入侵检测系统 (NIDS),用于保护车辆特设网络 (VANET). 这种新的方法在检测威胁方面达到99%的准确性,提高了道路安全.

关键词:
瓦内特 (Vanet) 是一个名字.卷积神经网络的神经网络.深度学习是一种深度学习.检测入侵 检测入侵长期短期记忆 长期短期记忆

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科学领域:

  • 网络安全 网络安全
  • 人工智能的人工智能
  • 智能运输系统 智能运输系统

背景情况:

  • 车辆特设网络 (VANET) 对智能交通系统至关重要,但由于拒绝服务 (DoS) 和分布式拒绝服务 (DDoS) 等攻击,它们面临着重大安全挑战.
  • 有效的网络入侵检测系统 (NIDS) 对于减轻这些威胁和确保道路安全至关重要.

研究的目的:

  • 开发一种基于人工智能 (AI) 的创新网络入侵检测系统 (NIDS),专门为VANET的独特安全需求设计.
  • 通过利用先进的深度学习技术,提高VANET入侵检测的性能和准确性.

主要方法:

  • 拟议的NIDS使用深度学习模型的组合:级联卷积神经网络 (CCNN) 用于高级特征提取和基于自我注意力的双向长短期记忆 (SA-BiLSTM) 进行分类.
  • 多变量梯度基础优化 (MV-GBO) 算法用于优化CCNN和SA-BiLSTM模型以及特征提取,进一步提高检测能力.
  • 模型性能在使用MATLAB平台的KDD-CUP99,ToN-IoT和VeReMi等既定数据集上进行了严格的评估.

主要成果:

  • 基于AI的NIDS表现出了卓越的性能,在所有评估的数据集中 (KDD-CUP99,ToN-IoT,VeReMi) 实现了99%的准确率.
  • 通过MV-GBO优化SA-BiLSTM和CCNN的集成,在识别VANET环境中的复杂网络入侵方面证明非常有效.
  • 基于MV-GBO的特征提取对提议模型的增强学习和检测精度做出了重大贡献.

结论:

  • 开发的基于AI的NIDS集成了CCNN,SA-BiLSTM和MV-GBO,为保护VANET免受复杂的网络威胁提供了强大的和高度准确的解决方案.
  • 这项研究强调了先进的深度学习技术在解决智能交通系统中关键安全挑战方面的潜力,从而提高了整体道路安全.
  • 拟议模型的99%准确性意味着安全关键的车辆网络入侵检测能力的实质性进步.