[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122980-en":3,"doc-seo-122980-105":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":20,"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},122980,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Machine Learning-Based Dynamic Attribute Selection Technique for DDoS Attack Classification in IoT Networks - Paper","The exponential growth of the Internet of Things (IoT) increases the exposure of interconnected devices to security threats, especially distributed denial-of-service (DDoS) attacks. The work presents a machine learning pipeline for DDoS detection in IoT networks, including data processing, a dynamic attribute selection module to choose adaptive features and reduce training time, and a classification module. Experiments use the CICI-IDS-2018 dataset and evaluate Decision Tree, Gaussian Naive Bayes, Logistic Regression, KNN, and Random Forest, showing strong performance.","computers   \nArticle  \nMachine Learning-Based Dynamic Attribute Selection Technique for DDoS Attack Classification in IoT Networks  \nSubhan Ullah 1, Zahid Mahmood 2, Nabeel Ali 3, Tahir Ahmad 4, * and Attaullah Buriro 5, *  \nCitation: Ullah, S.; Mahmood, Z.; Ali, N.; Ahmad, T.; Buriro, A. Machine Learning-Based Dynamic Attribute Selection Technique for DDoS Attack Classification in IoT Networks. Computers 2023, 12, 115 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)computers12060115  \nAcademic Editor: Paolo Bellavista  \nReceived: 9 March 2023  \nRevised: 9 May 2023  \nAccepted: 26 May 2023  \nPublished: 29 May 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Computer Science, National University of Computer and Emerging Sciences (NUCES-FAST), Islamabad 44000, Pakistan; [subhan.ullah@nu.edu.pk](subhan.ullah@nu.edu.pk)  \n2 Department of Computer Science and IT, University of Kotli Azad Jammu and Kashmir, Kotli 11100, Pakistan; [zahidmahmood575@uokajk.edu.pk](zahidmahmood575@uokajk.edu.pk)  \n3 Department of Electrical Engineering, Capital University of Science and Technology (CUST), Islamabad 44000, Pakistan; [mirza.nabeel.jarral@gmail.com](mirza.nabeel.jarral@gmail.com)  \n4 Center for Cybersecurity, Brunno Kessler Foundation, 38123 Trento, Italy  \n5 Faculty of Engineering, Free University Bozen-Bolzano, 39100 Bolzano, Italy  \n* Correspondence: [ahmad@fbk.eu](ahmad@fbk.eu) (T.A.); [attaullah.buriro@unibz.it](attaullah.buriro@unibz.it) (A.B.)  \nAbstract: The exponential growth of the Internet of Things (IoT) has led to the rapid expansion of interconnected systems, which has also increased the vulnerability of IoT devices to security threats such as distributed denial-of-service (DDoS) attacks. In this paper, we propose a machine learning pipeline that specifically addresses the issue of DDoS attack detection in IoT networks. Our approach comprises of (i) a processing module to prepare the data for further analysis,(ii) a dynamic attributeselection module that selects the most adaptive and productive features and reduces the training time, and (iii) a classification module to detect DDoS attacks. We evaluate the effectiveness of our approach using the CICI-IDS-2018 dataset and five powerful yet simple machine learning classifiers—Decision Tree (DT), Gaussian Naive Bayes, Logistic Regression (LR), K-Nearest Neighbor (KNN), and Random Forest (RF) . Our results demonstrate that DT outperforms its counterparts and achieves up to 99.98% accuracy in just 0.18 s of CPU time. Our approach is simple, lightweight, and accurate for detecting DDoS attacks in IoT networks.  \nKeywords: dynamic attribute selection; DDoS attack classification; CICI-IDS-2018 dataset  \n1. Introduction  \nThe Internet of Things (IoT) affects our lifestyle, including how we act and behave. It can be seen in the air conditioning that we can control through our smartphones, the EHealth care in which patients wear sensors on their bodies to track their health, and our intelligent watches that track our daily activities. IoT consists of many devices that are connected to a large network. These devices gather and share data. The IoT provided the world with an easy way of operating and monitoring their devices. With time, the use of the internet is growing. Therefore, IoT devices are growing in number. Consider where they are being used to understand how big it they have become. The use of IoT can be seen in industries and health departments. Business is changing in the way the paradigm shift in cloud computing operates because of IoT. This type of dependence of today's world on IoT can result in generating, monitoring, ","cbCailr7oIbXKQ0j","https://ap.wps.com/l/cbCailr7oIbXKQ0j","pdf",2145122,1,17,"English","en",105,"# Introduction\n## IoT growth and security risks\n## DDoS attack overview\n## Research focus and related approaches\n# Proposed Methodology\n## Pipeline subsystems: preprocessing, feature selection, detection\n## Dynamic attribute selection concept\n# Experimental Setup and Evaluation\n## Dataset: CICI-IDS-2018\n## Classifiers compared\n# Results and Discussion\n## Accuracy and CPU-time performance\n## Comparative effectiveness","[{\"question\":\"What problem does the proposed pipeline address?\",\"answer\":\"It targets efficient detection of distributed denial-of-service (DDoS) attacks in IoT networks, where attack traffic resembles normal traffic and is difficult to identify.\"},{\"question\":\"How does dynamic attribute selection contribute to the method?\",\"answer\":\"The dynamic attribute selection module selects the most adaptive and productive features, which reduces training time while improving detection capability.\"},{\"question\":\"Which dataset and classifiers are used for evaluation?\",\"answer\":\"Evaluation uses the CICI-IDS-2018 dataset and compares Decision Tree, Gaussian Naive Bayes, Logistic Regression, K-Nearest Neighbor, and Random Forest classifiers.\"}]","Machine Learning-Based Dynamic Attribute Selection Technique for DDoS Attack Classification in IoT Networks - 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