利用联合注意力神经网络进行边缘物联网入侵检测

IF 3.6 2区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Xiedong Song, Qinmin Ma
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引用次数: 0

摘要

边缘节点有望成长为一个价值数十亿美元的市场,对于检测物联网终端上的各种网络威胁至关重要。采用基于 FedACNN 的深度学习模型(DLM)的当前网络入侵检测系统受到了该网络设备层的资源限制。我们通过创建一种独特、轻量、快速、准确的边缘检测模型来识别边缘节点上基于 DLM 的分布式拒绝服务攻击,从而解决了这一问题。即使资源有限(如低功率、内存和处理能力),我们的方法也能以相应的速度生成真实结果。Federated Convolution Neural Network(FedACNN)深度学习方法采用注意机制,最大限度地减少了通信延迟。所开发的模型使用了最近部署在由树莓派(Raspberry Pi)模拟的边缘节点上的网络安全数据集(新南威尔士大学,2015 年)。我们的研究结果表明,与传统的 DLM 方法相比,我们的模型即使减少了 CPU 和内存资源的使用,仍能保持约 99% 的高准确率。此外,它的体积比最先进的模型小约三倍,而所需的测试时间却大大减少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intrusion Detection using Federated Attention Neural Network for Edge Enabled Internet of Things

Edge nodes, which are expected to grow into a multi-billion-dollar market, are essential for detection against a variety of cyber threats on Internet-of-Things endpoints. Adopting the current network intrusion detection system with deep learning models (DLM) based on FedACNN is constrained by the resource limitations of this network equipment layer. We solve this issue by creating a unique, lightweight, quick, and accurate edge detection model to identify DLM-based distributed denial service attacks on edge nodes. Our approach can generate real results at a relevant pace even with limited resources, such as low power, memory, and processing capabilities. The Federated Convolution Neural Network (FedACNN) deep learning method uses attention mechanisms to minimise communication delay. The developed model uses a recent cybersecurity dataset deployed on an edge node simulated by a Raspberry Pi (UNSW 2015). Our findings show that, compared to traditional DLM methodologies, our model retains a high accuracy rate of about 99%, even with decreased CPU and memory resource use. Also, it is about three times smaller in volume than the most advanced model while requiring a lot less testing time.

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来源期刊
Journal of Grid Computing
Journal of Grid Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
8.70
自引率
9.10%
发文量
34
审稿时长
>12 weeks
期刊介绍: Grid Computing is an emerging technology that enables large-scale resource sharing and coordinated problem solving within distributed, often loosely coordinated groups-what are sometimes termed "virtual organizations. By providing scalable, secure, high-performance mechanisms for discovering and negotiating access to remote resources, Grid technologies promise to make it possible for scientific collaborations to share resources on an unprecedented scale, and for geographically distributed groups to work together in ways that were previously impossible. Similar technologies are being adopted within industry, where they serve as important building blocks for emerging service provider infrastructures. Even though the advantages of this technology for classes of applications have been acknowledged, research in a variety of disciplines, including not only multiple domains of computer science (networking, middleware, programming, algorithms) but also application disciplines themselves, as well as such areas as sociology and economics, is needed to broaden the applicability and scope of the current body of knowledge.
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