一种新的基于区块链的物联网异常检测联邦学习方案

Van-Doan Nguyen;Abebe Diro;Naveen Chilamkurti;Will Heyne;Khoa Tran Phan
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引用次数: 0

摘要

在这项研究中,我们提出了一种新的异常检测系统(ADS),该系统将联邦学习(FL)与区块链相结合,用于资源受限的物联网。该系统允许物联网设备通过允许的区块链交换机器学习(ML)模型,通过模型共享实现可信的协作学习。为了避免单点故障,任何设备都可以成为FL过程的中心。为了解决物联网设备中的资源约束问题和FL中的模型中毒问题,我们引入了一种新的方法,在选择特定设备加入FL过程时使用承诺系数和ML模型差异。我们还提出了一种有效的启发式方法,从在选定设备上本地训练的ML模型列表中聚合联邦模型,这有助于提高联邦模型的异常检测能力。在物联网僵尸网络攻击检测常用的N-BaIoT数据集上进行的实验结果表明,本文提出的系统在检测异常和抵抗中毒攻击方面比两个基线(FedProx和fedag)更有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Blockchain-Enabled Federated Learning Scheme for IoT Anomaly Detection
In this research, we proposed a novel anomaly detection system (ADS) that integrates federated learning (FL) with blockchain for resource-constrained IoT. The proposed system allows IoT devices to exchange machine learning (ML) models through a permissioned blockchain, enabling trustworthy collaborative learning through model sharing. To avoid single-point failure, any device can be a centre of the FL process. To deal with the issue of resource constraints in IoT devices and the model poisoning problem in FL, we introduced a novel method to use commitment coefficients and ML model discrepancies when selecting particular devices to join the FL process. We also proposed an efficient heuristic method to aggregate a federated model from a list of ML models trained locally on the selected devices, which helps to improve the federated model’s anomaly detection ability. The experiment results with the popular N-BaIoT dataset for IoT botnet attack detection show that the proposed system is more effective in detecting anomalies and resisting poisoning attacks than the two baselines (FedProx and FedAvg).
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