用于检测大规模混合物联网网络 RPL 攻击的联合深度学习模型

IF 4.4 2区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Mohammed Albishari , Mingchu Li , Majid Ayoubi , Ala Alsanabani , Jiyu Tian
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引用次数: 0

摘要

随着物联网(IoT)的迅速普及,智能应用和服务变得越来越重要,使其成为容易获取个人身份信息的来源。在过去几年中,机器学习在确保路由层安全方面的应用,特别是低功耗和有损网络路由协议(RPL),已成为确保成功路由的基础,而隐私保护则是边缘节点的重要考虑因素。在最近的研究中,在中央服务器上对收集到的数据进行训练增加了人们对数据隐私的担忧。因此,分散学习是目前保护隐私的一种解决方案。它在物联网网络中越来越受欢迎,其中的模型是在边缘节点的混合数据上训练出来的,无需共享全局数据就能做出全局决策,这在权重更新时会造成很高的通信成本。我们提出了一种基于路由协议联合学习(Fed-RPL)的门控递归单元(GRU)模型,用于分散训练轮数和量化方法(Q-8bit),以减少权重更新次数,从而显著降低通信开销,并保持本地模型的高精确度。同时,集合单元汇总更新并选择最佳局部模型,以提高全局模型的准确性。我们的实验表明,在保护隐私的边缘数据方面,Fed-RPL 优于经典的机器学习(ML)方法,在非 IID 场景下显著降低了通信成本,并比最近的 FL 方法获得了更高的检测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Federated deep learning models for detecting RPL attacks on large-scale hybrid IoT networks
With the rapid spread of the Internet of Things (IoT), smart applications and services become increasingly crucial, making them an easily accessible source of personally identifiable information. Over the last few years, the use of machine learning in securing routing layers, particularly routing protocol for low-power and lossy networks (RPL), has become fundamental in ensuring successful routing and privacy preservation as a crucial consideration among edge nodes. In recent works, training of collected data on a central server has increased concerns regarding data privacy. Consequently, decentralized learning is currently a solution for privacy preservation. It has gained popularity in IoT networks in which the models are trained on hybrid data located in edge nodes and enable global decision-making without sharing global data, causing high communication costs during weight updates. We propose a federated learning of routing protocol (Fed-RPL)-based gated recurrent unit (GRU) model for decentralized training rounds and quantization method (Q-8bit) to decrease the number of weight updates that can significantly mitigate the communication overhead and maintain the local model with high accuracy. Meanwhile, the ensemble unit aggregates the updates and selects the best local model to enhance the global model accuracy. Our experiments show that Fed-RPL outperforms classical machine learning (ML) methods in privacy-preserving edge data, significantly reduces the communication cost in non-IID scenarios, and achieves higher detection accuracy than recent FL approaches.
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来源期刊
Computer Networks
Computer Networks 工程技术-电信学
CiteScore
10.80
自引率
3.60%
发文量
434
审稿时长
8.6 months
期刊介绍: Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.
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