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 Conference Paper Faculty Publications- Conference Papers 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 Vinodha, D.; Mary Anita, E.A.; Lapina, Maria Title A name given to the resource AI-Powered Botnet Detection Systems: A Critical Review of Current Approaches and Challenges Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1456 LNNS;pp.93-104 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-07275-7_10" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-07275-7_10</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105023167339?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105023167339?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 Vinodha D., Christ University, Bangalore, India; Mary Anita E.A., Christ University, Bangalore, India; Lapina M., North Caucasus Federal University, Stavropol, Russian Federation Description An account of the resource In the era of information technology, Botnets have become the most persistent cyber threat, capable of launching large-scale attacks like Distributed Denial of Service. stealing sensitive information and disturbing online services. Botnets have evolved from simple networks to complicated distributed networks including IoT devices, making them pervasive, harder to track, and destroy. Machine learning and Deep learning based models are emerging to detect bot attacks by analyzing large datasets and detecting patterns and anomalies. The state of the art methodologies for detecting bot infection are reviewed deeply and compared based on adopted methodologies, dataset and feature selection mechanism. The paper further discusses the pros and cons of existing methodologies. Finally, research gaps are presented to help future research on enhancing bot detection. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Botnet; Deep Learning; Internet of Things; Machine learning; network traffic analysis Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303207274-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online