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 Muloor, Kiran Hemanthraj; Sahu, Somesh; Samanta, Debabrata; Karuppiah, Marimuthu; Bhattacharya, Abhishek; Dutta, Soumi Title A name given to the resource Automate Threat Detection and Analysis Through Intelligent Data Mining Techniques for Network Traffic and Cybersecurity 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;1139;pp.11-19 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-7603-0_2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-7603-0_2</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85218466538?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85218466538?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 Muloor K.H., LTIMindtree Limited, Karnataka, Bangalore, India, Department of Computer Science, CHRIST University, Karnataka, Bangalore, India; Sahu S., LTIMindtree Limited, Karnataka, Bangalore, India; Samanta D., Department of Computing and Information Technologies, Rochester Institute of Technology, Pristina, Kosovo; Karuppiah M., School of Computer Science and Engineering and Information Science, Presidency University, Karnataka, Bengaluru, 560064, India; Bhattacharya A., Sister Nivedita University, Kolkata, India; Dutta S., Sister Nivedita University, Kolkata, India Description An account of the resource Today, we are constantly surrounded by vast amounts of data, a trend that is expected to grow significantly over the next decade. The abundance of data presents challenges for thorough analysis and extraction of valuable insights buried within unstructured information. Advanced tools like data mining are crucial in uncovering this useful information and making full use of it. In light of the increasing number of security threats in networks, there is a need for robust security solutions. While traditional network security measures have been primarily managed locally, concerns about internet-based security have grown due to heightened computer usage leading to cybercriminal activities previously limited to physical intrusions. A threat intelligence program aims to enhance analytical and preventive capabilities by acquiring knowledge about potential or existing threats based on evidence. As most devices are interconnected with the Internet, many organizations prioritize cybersecurity as they acknowledge the vulnerabilities arising from this connectivityproviding opportunities for cyber-attacks. Effective threat intelligence concerning network traffic necessitates a comprehensive understanding supported by thoughtful representation techniques. This paper proposes an extensive exploration of various machine learning methods aimed at identifying weaknesses in detecting invasive activity using different approaches and evaluating their performance against the KDD 99 benchmark dataset. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Classification; Clustering; Intelligent threat detection; KDD; Machine learning; Time series Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981977602-3; 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