• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE transactions on industrial informatics >DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber–Physical Systems
【24h】

DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber–Physical Systems

机译：深度：联邦深度学习在工业网络系统中的入侵检测

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

The rapid convergence of legacy industrial infrastructures with intelligent networking and computing technologies (e.g., 5G, software-defined networking, and artificial intelligence), have dramatically increased the attack surface of industrial cyber-physical systems (CPSs). However, withstanding cyber threats to such large-scale, complex, and heterogeneous industrial CPSs has been extremely challenging, due to the insufficiency of high-quality attack examples. In this article, we propose a novel federated deep learning scheme, named DeepFed, to detect cyber threats against industrial CPSs. Specifically, we first design a new deep learning-based intrusion detection model for industrial CPSs, by making use of a convolutional neural network and a gated recurrent unit. Second, we develop a federated learning framework, allowing multiple industrial CPSs to collectively build a comprehensive intrusion detection model in a privacy-preserving way. Further, a Paillier cryptosystem-based secure communication protocol is crafted to preserve the security and privacy of model parameters through the training process. Extensive experiments on a real industrial CPS dataset demonstrate the high effectiveness of the proposed DeepFed scheme in detecting various types of cyber threats to industrial CPSs and the superiorities over state-of-the-art schemes.
机译：传统工业基础设施的快速收敛性与智能网络和计算技术（例如，5G，软件定义的网络和人工智能）大大增加了工业网络物理系统（CPS）的攻击面。然而，由于高质量攻击示例的功能不足，对这种大规模，复杂和异质工业CPS的威胁具有极大的挑战性。在本文中，我们提出了一种名为Deplefed的新型联合学习计划，以检测对工业CPS的网络威胁。具体而言，我们首先通过利用卷积神经网络和门控复发单元设计了工业CPS的新深度学习的入侵检测模型。其次，我们开发联合学习框架，允许多个工业CPS以隐私保存方式共同构建全面的入侵检测模型。此外，精制了基于Paillier密码系统的安全通信协议，以通过培训过程保留模型参数的安全性和隐私。在真正的工业CPS数据集上进行了广泛的实验，证明了拟议的深度方案的高效性，在检测到工业CPS的各种网络威胁以及最先进的计划中的优势。

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