[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123098-en":3,"doc-seo-123098-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123098,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Comparative Analysis of Machine Learning Techniques for DDoS Intrusion Detection in IoT Environments - Research","This study evaluates the effectiveness of Intrusion Detection Systems (IDS) against Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) environments using machine-learning methods. It addresses the need for lightweight classifiers that separate legitimate from malicious traffic without high computational cost. Four models are compared—k-NN, SVM, Random Forest, and Multilayer Perceptron—followed by a proposed minimalistic MLP-based μML-IDS model. Tested on the CICIDS2017 dataset, it achieves 99.8% accuracy with strong F-score and precision, indicating efficient real-world viability for protecting resource-limited IoT networks.","Chukwukelu,G.,Essien,A.,Salami,A.I.,&Utuk,E.(2024).Comparative Analysis of Machine Learning Techniques for DDoSIntrusion Detection in loT Environments.In S.Hammoudi,F.Wijnhoven,&A.Emrouznejad (Eds.),Proceedings of the 21stInternational Conference on Smart Business Technologies,ICSBT2024(pp.19-27).(ICSBT International Conference on Smart BusinessTechnologies).Science and Technology Publications,Lda.https://doi.org/10.5220/0012765200003764  \nPublisher's PDF,also known as Version of recordLicense (if available):CC BY-NC-ND  \nLink to published version (if available):  \n10.5220/0012765200003764  \nLink to publication record on the Bristol Research PortalPDF-document  \nUniversity of Bristol-Bristol Research PortalGeneral rights  \nThis document is made available in accordance with publisher policies.Please cite only thepublished version using the reference above.Full terms of use are available:http://www.bristol.ac.uk/red/research-policy/pureluser-guides/brp-terms/  \n# Comparative Analysis of Machine Learning Techniques for DDoSIntrusion Detection in IoT Environments\n\nGodwin Chukwukelu¹D,Aniekan Essien²O,Adewale Imram Salami³and Esther Utuk⁴  \nIIT Services Department,Forctix Ltd,London,U.K.  \n²Operations and Management Science,Healthcare &Innovation Group,University of Bristol,Bristol,U.K.³Department of Computer Science,Leadcity University,Ibadan,Nigeria4University of East AngliaNorwich,U.K.  \nKeywords:Intrusion Detection System(IDS),Distributed Denial of Service(DDoS),Internet of Things (IoT),Machine Learning Algorithms.  \nAbstract:This study addresses the challenge of Distributed Denial of Service(DDoS)attacks in the Internet of Things(IoT)environment by evaluating the effectiveness of Intrusion Detection Systems (IDS)using machinelearning techniques.Due to the lightweight computational configuration of IoT systems,there is a need for aclassifier that can efficiently distinguish between legitimate and malicious network traffic without demandingsubstantial computational resources.This research presents a comparative analysis of four machine learningmodels:(i)k-Nearest Neighbour(k-NN),(ii)Support Vector Machine (SVM),(ii)Random Forest(RF),and(iv)Multilayer Perceptron(MLP),to propose a lightweight DDoS intrusion detection classifier.A novelclassification model based on the MLP architecture is proposed,focusing on minimalistic design and featurereduction to achieve accurate and efficient classification.The model is tested using the CICIDS2017 datasetand demonstrates high accuracy and computational efficiency,making it a viable solution for IoTenvironments where computational resources are limited.The findings show that the proposed μML-IDSmodel achieves an accuracy of 99.8%,F-score of 96.5%,and precision of 99.96%,with minimalcomputational overhead,highlighting its potential for real-world application in protecting IoT networksagainst DDoS attacks  \ndevices,such as smart bulbs,doors,and TVs are alsovulnerable,posing risks of financial loss,privacybreach,and data theft(Verma&Ranga,2020).  \n## 1 INTRODUCTION\n\nThe advent of the Internet of Things (IoT)hastransformed everyday objects into interconnectedsmart devices,creating a network of over 20 billiondevices globally((Al-Hadhrami &Hussain,2021).This rapid proliferation,fuelled by advancements inIP addressing and affordable microcontrollers,hasnot only enhanced connectivity but also exposedthese devices to diverse cyber threats,notablyDistributed Denial of Service(DDoS)attacks(Salimet al.,2020).The impact of such attacks can becatastrophic,especially when targeting criticalnational infrastructures like healthcare systems,where a cyberattack can lead to devastatingconsequences,including loss of life(Willing et al,2021).Besides critical infrastructures,common IoT  \nGiven these emerging threats,this research isdriven by the need to reinforce the security of IoTnetworks.The study focuses on understanding therole and effectiveness of Intrusion Detection Systems(IDS)in safeguarding IoT-connected","cbCaigt61axdWFTR","https://ap.wps.com/l/cbCaigt61axdWFTR","pdf",944862,1,10,"English","en",105,"# 1 INTRODUCTION\n## IoT threats and DDoS impact\n## Role of IDS in IoT network security\n## Research contributions and μML-IDS focus","[{\"question\":\"Why is a lightweight intrusion detection classifier needed for IoT environments?\",\"answer\":\"IoT devices have lightweight computational constraints, so the classifier must distinguish malicious from legitimate traffic without requiring substantial computational resources.\"},{\"question\":\"Which machine learning models are compared in this research?\",\"answer\":\"The study compares k-Nearest Neighbour (k-NN), Support Vector Machine (SVM), Random Forest (RF), and Multilayer Perceptron (MLP) for DDoS intrusion detection.\"},{\"question\":\"What performance does the proposed μML-IDS model achieve on the CICIDS2017 dataset?\",\"answer\":\"The μML-IDS model is reported to achieve 99.8% accuracy, an F-score of 96.5%, and precision of 99.96%, with minimal computational overhead.\"}]","Comparative Analysis of Machine Learning Techniques for DDoS Intrusion Detection in IoT Environments - Research | 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is a lightweight intrusion detection classifier needed for IoT environments?","Question",{"text":75,"@type":76},"IoT devices have lightweight computational constraints, so the classifier must distinguish malicious from legitimate traffic without requiring substantial computational resources.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are compared in this research?",{"text":80,"@type":76},"The study compares k-Nearest Neighbour (k-NN), Support Vector Machine (SVM), Random Forest (RF), and Multilayer Perceptron (MLP) for DDoS intrusion detection.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance does the proposed μML-IDS model achieve on the CICIDS2017 dataset?",{"text":84,"@type":76},"The μML-IDS model is reported to achieve 99.8% accuracy, an F-score of 96.5%, and precision of 99.96%, with minimal computational 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