Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Madavi K. P, Bindu; Sowjanya K, Krishna; Sardar, Tanvir Habib; Shafeeq B. M, Ahamed Title A name given to the resource Online alert system for DDoS attack detection and prevention using machine learning classification algorithms Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Cogent Engineering;Volume;12;Issue;1;Article No.;2588895; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1080/23311916.2025.2588895" target="_blank" rel="noreferrer noopener">https://doi.org/10.1080/23311916.2025.2588895</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022474600?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022474600?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Madavi K. P B., Department of Computer Science and Engineering, Christ University, Karnataka, Bangalore, India; Sowjanya K K., Department of Computer Science and Engineering, CMR Institute of Technology, Karnataka, Bengaluru, India; Sardar T.H., Dept. of CSE, School of Engineering, Dayananda Sagar University, Bangalore, India; Shafeeq B. M A., Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India Description An account of the resource Distributed Denial of Service (DDoS) attack makes a server inaccessible by flooding it with fallacious traffic. It uses many intermediate devices such as computers, servers, smartphones, and even IoT Devices to generate false traffic. These attacks become more threatening if the attackers use any of these devices to have access to WiFi routers, security cameras, smart devices, etc. This paper proposes a model for DDoS attack detection and mitigation that identifies the DDoS attack and alerts the administrative authorities with the help of machine learning classification algorithms. The paper surveys discrete types of Machine Learning algorithms to identify and mitigate the DDoS attack. Three labeled datasets are employed in this paper to train the model for effective DDOS attack detection with better accuracy. These data sets comprises of benign and malignant attacks to train and test the classification algorithms. Based on the experimental results and performance metrics, it is identified that the XGBoost algorithm provided better accuracy of 99.8% on all three labeled datasets. 2025 The Author(s). Published by Informa UK Limited, trading as Taylor &amp; Francis Group. Subject The topic of the resource Attacks; Denial-of-Service (DoS) attacks; distributed Denial-of-Service (DDoS); machine learning algorithms; servers Publisher An entity responsible for making the resource available Cogent OA Relation A related resource ISSN: 23311916; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online