QAE-IDS: DDoS anomaly detection in IoT devices using Post-Quantization Training

IF 2.4 Q2 MULTIDISCIPLINARY SCIENCES
B. S. Sharmila, Rohini Nagapadma
{"title":"QAE-IDS: DDoS anomaly detection in IoT devices using Post-Quantization Training","authors":"B. S. Sharmila, Rohini Nagapadma","doi":"10.1080/23080477.2023.2260023","DOIUrl":null,"url":null,"abstract":"ABSTRACTOver the past few years, many intellectuals have focused on unsupervised learning for anomaly detection in IoT networks. Deploying an unsupervised Autoencoder algorithm for Intrusion Detection System (IDS) is computationally intensive for IoT devices with limited resources. In this work, we propose two distinct AI models using Post-Training Quantization; Quantized Autoencoder float16 (QAE-float16) and Quantized Autoencoder uint8 (QAE-uint8). QAE models are derived using Autoencoder models, which work on the assumption of generating high Reconstruction Error (RE) for anomaly data. Post Training Quantization includes pruning, clustering, and Quantization techniques. The proposed models were tested against the RT-IoT23 dataset, which includes normal and attack traces. This study is focused on the three major types of attacks, namely SSH brute-force, UFONet and DDoS (Distributed Denial of Service) exploitation. Since these attacks are the gateway for future exploitation. The model performance evaluated on IoT devices reveals that QAE-uint8 is the most efficient model by a wide margin, with average memory utilization decreased by 70.01%, memory size compressed by 92.23%, and peak CPU utilization decreased by 27.94%. Therefore, the proposed QAE-uint8 model has the potential to be used in low-power IoT Edge devices to detect anomalies.KEYWORDS: Anomaly detectionartificial intelligenceautoencodersIoTIDSpost-quantization training Disclosure statementNo potential conflit of interest was reported by the authors.","PeriodicalId":53436,"journal":{"name":"Smart Science","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Smart Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/23080477.2023.2260023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

ABSTRACTOver the past few years, many intellectuals have focused on unsupervised learning for anomaly detection in IoT networks. Deploying an unsupervised Autoencoder algorithm for Intrusion Detection System (IDS) is computationally intensive for IoT devices with limited resources. In this work, we propose two distinct AI models using Post-Training Quantization; Quantized Autoencoder float16 (QAE-float16) and Quantized Autoencoder uint8 (QAE-uint8). QAE models are derived using Autoencoder models, which work on the assumption of generating high Reconstruction Error (RE) for anomaly data. Post Training Quantization includes pruning, clustering, and Quantization techniques. The proposed models were tested against the RT-IoT23 dataset, which includes normal and attack traces. This study is focused on the three major types of attacks, namely SSH brute-force, UFONet and DDoS (Distributed Denial of Service) exploitation. Since these attacks are the gateway for future exploitation. The model performance evaluated on IoT devices reveals that QAE-uint8 is the most efficient model by a wide margin, with average memory utilization decreased by 70.01%, memory size compressed by 92.23%, and peak CPU utilization decreased by 27.94%. Therefore, the proposed QAE-uint8 model has the potential to be used in low-power IoT Edge devices to detect anomalies.KEYWORDS: Anomaly detectionartificial intelligenceautoencodersIoTIDSpost-quantization training Disclosure statementNo potential conflit of interest was reported by the authors.
QAE-IDS:使用后量化训练的物联网设备中的DDoS异常检测
在过去的几年里,许多知识分子都在关注物联网网络中用于异常检测的无监督学习。为入侵检测系统(IDS)部署无监督自编码器算法对于资源有限的物联网设备来说是计算密集型的。在这项工作中,我们使用训练后量化提出了两种不同的人工智能模型;量化的自动编码器float16 (QAE-float16)和量化的自动编码器uint8 (QAE-uint8)。基于对异常数据产生高重构误差(RE)的假设,利用自编码器模型推导出了QAE模型。培训后量化包括修剪、聚类和量化技术。提出的模型针对RT-IoT23数据集进行了测试,其中包括正常和攻击痕迹。本研究的重点是三种主要的攻击类型,即SSH暴力破解、UFONet和DDoS(分布式拒绝服务)利用。因为这些攻击是未来利用的门户。在物联网设备上评估的模型性能表明,QAE-uint8是最有效的模型,平均内存利用率降低了70.01%,内存大小压缩了92.23%,峰值CPU利用率降低了27.94%。因此,提出的QAE-uint8模型有潜力用于低功耗物联网边缘设备来检测异常。关键词:异常检测人工智能自动编码器siotidpost -量化训练披露声明作者未报告潜在利益冲突
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
Smart Science
Smart Science Engineering-Engineering (all)
CiteScore
4.70
自引率
4.30%
发文量
21
期刊介绍: Smart Science (ISSN 2308-0477) is an international, peer-reviewed journal that publishes significant original scientific researches, and reviews and analyses of current research and science policy. We welcome submissions of high quality papers from all fields of science and from any source. Articles of an interdisciplinary nature are particularly welcomed. Smart Science aims to be among the top multidisciplinary journals covering a broad spectrum of smart topics in the fields of materials science, chemistry, physics, engineering, medicine, and biology. Smart Science is currently focusing on the topics of Smart Manufacturing (CPS, IoT and AI) for Industry 4.0, Smart Energy and Smart Chemistry and Materials. Other specific research areas covered by the journal include, but are not limited to: 1. Smart Science in the Future 2. Smart Manufacturing: -Cyber-Physical System (CPS) -Internet of Things (IoT) and Internet of Brain (IoB) -Artificial Intelligence -Smart Computing -Smart Design/Machine -Smart Sensing -Smart Information and Networks 3. Smart Energy and Thermal/Fluidic Science 4. Smart Chemistry and Materials
×
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