Implementation of Lightweight Machine Learning-Based Intrusion Detection System on IoT Devices of Smart Homes

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Abbas Javed, Amna Ehtsham, Muhammad Jawad, Muhammad Naeem Awais, Ayyaz-Ul-Haq Qureshi, Hadi Larijani
{"title":"Implementation of Lightweight Machine Learning-Based Intrusion Detection System on IoT Devices of Smart Homes","authors":"Abbas Javed, Amna Ehtsham, Muhammad Jawad, Muhammad Naeem Awais, Ayyaz-Ul-Haq Qureshi, Hadi Larijani","doi":"10.3390/fi16060200","DOIUrl":null,"url":null,"abstract":"Smart home devices, also known as IoT devices, provide significant convenience; however, they also present opportunities for attackers to jeopardize homeowners’ security and privacy. Securing these IoT devices is a formidable challenge because of their limited computational resources. Machine learning-based intrusion detection systems (IDSs) have been implemented on the edge and the cloud; however, IDSs have not been embedded in IoT devices. To address this, we propose a novel machine learning-based two-layered IDS for smart home IoT devices, enhancing accuracy and computational efficiency. The first layer of the proposed IDS is deployed on a microcontroller-based smart thermostat, which uploads the data to a website hosted on a cloud server. The second layer of the IDS is deployed on the cloud side for classification of attacks. The proposed IDS can detect the threats with an accuracy of 99.50% at cloud level (multiclassification). For real-time testing, we implemented the Raspberry Pi 4-based adversary to generate a dataset for man-in-the-middle (MITM) and denial of service (DoS) attacks on smart thermostats. The results show that the XGBoost-based IDS detects MITM and DoS attacks in 3.51 ms on a smart thermostat with an accuracy of 97.59%.","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":"316 5","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/fi16060200","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
引用次数: 0

Abstract

Smart home devices, also known as IoT devices, provide significant convenience; however, they also present opportunities for attackers to jeopardize homeowners’ security and privacy. Securing these IoT devices is a formidable challenge because of their limited computational resources. Machine learning-based intrusion detection systems (IDSs) have been implemented on the edge and the cloud; however, IDSs have not been embedded in IoT devices. To address this, we propose a novel machine learning-based two-layered IDS for smart home IoT devices, enhancing accuracy and computational efficiency. The first layer of the proposed IDS is deployed on a microcontroller-based smart thermostat, which uploads the data to a website hosted on a cloud server. The second layer of the IDS is deployed on the cloud side for classification of attacks. The proposed IDS can detect the threats with an accuracy of 99.50% at cloud level (multiclassification). For real-time testing, we implemented the Raspberry Pi 4-based adversary to generate a dataset for man-in-the-middle (MITM) and denial of service (DoS) attacks on smart thermostats. The results show that the XGBoost-based IDS detects MITM and DoS attacks in 3.51 ms on a smart thermostat with an accuracy of 97.59%.
在智能家居物联网设备上实现基于机器学习的轻量级入侵检测系统
智能家居设备(又称物联网设备)为人们提供了极大的便利,但同时也为攻击者提供了危害业主安全和隐私的机会。由于计算资源有限,确保这些物联网设备的安全是一项艰巨的挑战。基于机器学习的入侵检测系统(IDS)已在边缘和云端实施,但 IDS 尚未嵌入物联网设备。针对这一问题,我们为智能家居物联网设备提出了一种新颖的基于机器学习的双层 IDS,以提高准确性和计算效率。所提 IDS 的第一层部署在基于微控制器的智能恒温器上,该恒温器会将数据上传到云服务器托管的网站上。IDS 的第二层部署在云端,用于对攻击进行分类。所提出的 IDS 在云端检测威胁的准确率可达 99.50%(多分类)。为了进行实时测试,我们实施了基于 Raspberry Pi 4 的对手,以生成针对智能恒温器的中间人(MITM)和拒绝服务(DoS)攻击的数据集。结果表明,基于 XGBoost 的 IDS 能在 3.51 毫秒内检测到智能恒温器上的 MITM 和 DoS 攻击,准确率高达 97.59%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
期刊介绍: ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信