...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Network Science and Engineering, IEEE Transactions on >Data-Driven Intrusion Detection for Intelligent Internet of Vehicles: A Deep Convolutional Neural Network-Based Method
【24h】

Data-Driven Intrusion Detection for Intelligent Internet of Vehicles: A Deep Convolutional Neural Network-Based Method

机译：智能车辆智能互联网的数据驱动入侵检测：基于深度卷积神经网络的方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

As an industrial application of Internet of Things (IoT), Internet of Vehicles (IoV) is one of the most crucial techniques for Intelligent Transportation System (ITS), which is a basic element of smart cities. The primary issue for the deployment of ITS based on IoV is the security for both users and infrastructures. The Intrusion Detection System (IDS) is important for IoV users to keep them away from various attacks via the malware and ensure the security of users and infrastructures. In this paper, we design a data-driven IDS by analyzing the link load behaviors of the Road Side Unit (RSU) in the IoV against various attacks leading to the irregular fluctuations of traffic flows. A deep learning architecture based on the Convolutional Neural Network (CNN) is designed to extract the features of link loads, and detect the intrusion aiming at RSUs. The proposed architecture is composed of a traditional CNN and a fundamental error term in view of the convergence of the backpropagation algorithm. Meanwhile, a theoretical analysis of the convergence is provided by the probabilistic representation for the proposed CNN-based deep architecture. We finally evaluate the accuracy of our method by way of implementing it over the testbed.
机译：作为物联网（物联网）的工业应用，车辆（IOV）是智能交通系统（其）最重要的技术之一，这是智能城市的基本要素。基于IOV部署的主要问题是用户和基础架构的安全性。入侵检测系统（IDS）对于IOV用户非常重要，以通过恶意软件将其远离各种攻击，并确保用户和基础架构的安全性。在本文中，我们通过分析IOV中的路边单元（RSU）的链路负载行为来针对各种攻击来设计数据驱动的ID，导致交通流量不规则波动。基于卷积神经网络（CNN）的深度学习架构旨在提取链路载荷的特征，并检测旨在RSU的入侵。考虑到BackPropagation算法的收敛，所提出的架构由传统的CNN和基本误差术语组成。同时，通过拟议的基于CNN的深度架构提供了对收敛的理论分析。我们终于通过在测试平台上实现了方法来评估了我们的方法的准确性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号