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 Sowmya, T.; Anita, E. A. Mary Title A name given to the resource A Novel SHiP Vector Machine for Network Intrusion Detection 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 IEEE Access;Volume;13;pp.117445-117463 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ACCESS.2025.3586296" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ACCESS.2025.3586296</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010190732?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010190732?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 Sowmya T., Manipal Academy of Higher Education, Manipal Institute of Technology, Manipal, Bengaluru, 576104, India, Christ University, School of Engineering and Technology, Department of Computer Science and Engineering, Bengaluru, 560074, India; Anita E.A.M., Christ University, Department of Computer Science and Engineering, Karnataka, Bengaluru, 560074, India Description An account of the resource In this paper, network intrusion detection is proposed using an improved version of the support vector machine model to detect DoS attacks. Here, the SVM model considers the weight parameter along with the kernel to find the best decision boundary that separates the data into DoS and normal. The proposed model provides a novel kernel trick that reduces the overlapping of data. The intrusion detection system aims to construct an ideal system that can detect attacks with very high performance using a ShiP vector machine(Sophisticated High Performance Vector Machine). The framework comprises three major steps: data collection and preprocessing, Recursive Feature Elimination (RFE) based feature selection, and the ShiP Vector Machine classification strategy. The system is evaluated using the DoS dataset from UNSWNB15 and real time PSD-23 sniffer dataset. DoS data is generated by extracting the normal and DoS attacks from the UNSWNB15 dataset. Experimental results show that the proposed ShiP vector machine shows outstanding performance by achieving 96.44 % accuracy on the DoS dataset and 90.12 % accuracy for real time PSD-23 data. 2013 IEEE. Subject The topic of the resource DoS dataset recursive feature elimination (RFE); PSD-23 dataset; SHiP vector machine (sophisticated high performance vector machine) Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISSN: 21693536; 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