相关实验视频

Updated: May 3, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

NIDS-FGPA:基于梯度相似性模型的安全聚合的联合学习网络入侵检测算法

JiaMing Wang¹, Kai Yang¹, MinJing Li²

¹Xi'an Key Laboratory of Human-Machine Integration and Control Technology for Intelligent Rehabilitation, School of Computer Science, Xijing University, Xi'an, P.R. China.

|October 24, 2024

概括

本研究介绍了一种用于工业物联网 (IIoT) 安全的新型入侵检测系统. 联合学习方法通过有效处理不完整的数据和减少通信开销来提高网络安全性.

科学领域:

网络安全网络安全
网络安全网络安全
事物的工业互联网 (IIoT)

背景情况:

由于网络威胁的增加,IIoT安全性至关重要.
现有的入侵检测系统与不完整的数据,缺失的标签和高通讯开销作斗争.
在IIoT环境中需要强大而安全的入侵检测.

研究的目的:

为IIoT安全提出一个基于学习的联合入侵检测算法 (NIDS-FGPA).
为了应对不完整数据,缺少标签和通信开销的挑战.
提高IIoT网络的安全性和效率.

主要方法:

联合学习架构与Paillier同态加密相结合,可提供安全的训练.
梯度相似模型聚合 (GSA) 通过动态模型更新选择和加权来减少通信开销.
一个深度学习模型,利用二维卷积神经网络和双向封闭循环单元 (2DCNN-BIGRU) 来处理复杂的网络流量数据.

主要成果:

在Edge-IIoTset数据集上实现了94.5%的准确性.
在CIC IoT 2023数据集上实现了99.2%的准确性.
具备识别和捕获复杂网络攻击的能力.

结论:

该NIDS-FGPA模型有效地提高了IIoT网络安全.
提出的方法成功地解决了现有的入侵检测系统的局限性.
联合学习与同态加密和GSA提供了一个有前途的解决方案,用于安全和高效的IIoT入侵检测.

更多相关视频

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

相关概念视频

Aggregates Classification

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...