物联网入侵检测的联邦学习

IF 2.5 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Ai Magazine Pub Date : 2023-07-24 DOI:10.3390/ai4030028
Riccardo Lazzarini, H. Tianfield, V. Charissis
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引用次数: 1

摘要

物联网(IoT)设备的数量在过去几年中大幅增加,导致对物联网基础设施的网络攻击大量增加。作为网络安全深度防御方法的一部分,入侵检测系统(ids)在有效检测恶意活动方面发挥了关键作用。物联网中大多数现代IDS方法都是基于机器学习(ML)技术。其中大多数是集中式的,这意味着将数据从源设备共享到中央服务器以进行分类。这就提出了与用户数据隐私相关的潜在关键问题,以及由于数据量而导致的数据传输挑战。在本文中,我们评估了联邦学习(FL)作为在物联网环境中实现入侵检测的方法的使用。FL是集中式机器学习模型的另一种分布式方法,最近对物联网入侵检测的兴趣激增。在我们的实现中,我们使用浅人工神经网络(ANN)作为共享模型和联邦平均(FedAvg)作为聚合算法来评估FL。在ToN_IoT和CICIDS2017数据集上完成了二值分类和多类分类实验。分类由分布式设备使用它们自己的数据执行。参与者之间不会共享数据,维护数据隐私。与集中式方法相比,结果表明,在准确性、精密度、召回率和f1分数方面,协作式FL IDS是一种有效的替代方案,使其成为物联网IDS的可行选择。此外,以这些结果为基准,我们使用Flower FL框架在相同的设置下评估了备选聚合算法,即FedAvgM, FedAdam和FedAdagrad。评估结果表明,在我们的场景中,FedAvg和FedAvgM比FedAdam和FedAdagrad两种自适应算法表现得更好。
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Federated Learning for IoT Intrusion Detection
The number of Internet of Things (IoT) devices has increased considerably in the past few years, resulting in a large growth of cyber attacks on IoT infrastructure. As part of a defense in depth approach to cybersecurity, intrusion detection systems (IDSs) have acquired a key role in attempting to detect malicious activities efficiently. Most modern approaches to IDS in IoT are based on machine learning (ML) techniques. The majority of these are centralized, which implies the sharing of data from source devices to a central server for classification. This presents potentially crucial issues related to privacy of user data as well as challenges in data transfers due to their volumes. In this article, we evaluate the use of federated learning (FL) as a method to implement intrusion detection in IoT environments. FL is an alternative, distributed method to centralized ML models, which has seen a surge of interest in IoT intrusion detection recently. In our implementation, we evaluate FL using a shallow artificial neural network (ANN) as the shared model and federated averaging (FedAvg) as the aggregation algorithm. The experiments are completed on the ToN_IoT and CICIDS2017 datasets in binary and multiclass classification. Classification is performed by the distributed devices using their own data. No sharing of data occurs among participants, maintaining data privacy. When compared against a centralized approach, results have shown that a collaborative FL IDS can be an efficient alternative, in terms of accuracy, precision, recall and F1-score, making it a viable option as an IoT IDS. Additionally, with these results as baseline, we have evaluated alternative aggregation algorithms, namely FedAvgM, FedAdam and FedAdagrad, in the same setting by using the Flower FL framework. The results from the evaluation show that, in our scenario, FedAvg and FedAvgM tend to perform better compared to the two adaptive algorithms, FedAdam and FedAdagrad.
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来源期刊
Ai Magazine
Ai Magazine 工程技术-计算机:人工智能
CiteScore
3.90
自引率
11.10%
发文量
61
审稿时长
>12 weeks
期刊介绍: AI Magazine publishes original articles that are reasonably self-contained and aimed at a broad spectrum of the AI community. Technical content should be kept to a minimum. In general, the magazine does not publish articles that have been published elsewhere in whole or in part. The magazine welcomes the contribution of articles on the theory and practice of AI as well as general survey articles, tutorial articles on timely topics, conference or symposia or workshop reports, and timely columns on topics of interest to AI scientists.
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