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 Fernandes, Melsa Magdalene; Pereira, Sharlene Anna; Anita, EA Mary; Sowmya, T. Title A name given to the resource Efficient Intrusion Detection through Class Balancing and Feature Selection: A Case Study with SVM 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 Lecture Notes in Networks and Systems;Volume;1296 LNNS;pp.1-9 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-3314-2_1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-3314-2_1</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105009242266?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105009242266?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 Fernandes M.M., Department of CSE, Christ University, Bangalore Kengeri Campus Kanmanike, Karnataka, Bangalore, India; Pereira S.A., Department of CSE, Christ University, Bangalore Kengeri Campus Kanmanike, Karnataka, Bangalore, India; Anita E.A.M., Department of CSE, Christ University, Bangalore Kengeri Campus Kanmanike, Karnataka, Bangalore, India; Sowmya T., Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Karnataka, Manipal, India Description An account of the resource Intrusion Detection Systems are of paramount importance in network security. However, in real-world scenarios, they always suffer from the challenge of class imbalance, which is dominated by normal traffic. This paper presents a novel approach to enhancing the performance of IDS by proposing a hybrid of the Random Under sampling technique with the univariate feature selection technique, SelectKBest, for handling both problems of class imbalance and high dimensionality. This model was hence tried on the Bot-IoT dataset, which is a real-world IoT network traffic representation. The SVM classifier, which has been trained with the resampled and feature-selected data, showcased 95% balanced accuracy for both normal and malicious traffic detection. The combination of RUS and SelectKBest, apart from reducing overfitting, ensured the retention of the most relevant features and thereby made the IDS model robust. It can practically enhance the performance of IDS in an imbalanced and high-dimensional dataset by providing a balanced, efficient, and precise detecting mechanism. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Bot-IoT dataset; Class imbalance; Feature selection; Intrusion Detection Systems (IDS); Random under sampling (RUS); SelectKBest; Support Vector Machine (SVM) Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981963313-5; 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