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 Verma, Narinder; Kumar, Neerendra; Singh, Kuljeet; Aljohani, Abeer; Sinha, Anurag; Hussain, Syed Abid Title A name given to the resource A novel univariate feature selection with ANOVA F-test-based machine learning model for Intrusion Detection Framework of Robotics system 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 Applied Artificial Intelligence;Volume;39;Issue;1;Article No.;2539395; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1080/08839514.2025.2539395" target="_blank" rel="noreferrer noopener">https://doi.org/10.1080/08839514.2025.2539395</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105013234445?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105013234445?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 Verma N., Department of Computer Science and IT, Central University of Jammu, Jammu, India; Kumar N., Department of Computer Science and IT, Central University of Jammu, Jammu, India; Singh K., Department of Computer Science, Christ University, Delhi, India; Aljohani A., Department of Computer Science, Applied College, Taibah University, Medina, Saudi Arabia; Sinha A., Tech School, Computer Science Department, ICFAI University, Jharkhand, Ranchi, India; Hussain S.A., Department of Computer Science and Engineering, Bakhtar University, Kabul, Afghanistan Description An account of the resource Robotic systems have become popular across various industries, ranging from manufacturing and healthcare to logistics and space exploration. However, increasing the integration of robotic systems into critical infrastructures exposes devices to cybersecurity threats. The intrusion detection system (IDS) plays a vital role in safeguarding the systems from malicious activities and unauthorized access. This paper presents a novel, robotics-aware IDS framework incorporating hybrid feature selection and tailored classification strategies for robotic system. To evaluate the efficacy of the presented framework, an algorithm is also designed and tested using multiple machine-learning techniques. The NSL-KDD dataset is utilized for training and evaluating machine learning models due to the inclusion of a wide range of attack scenarios and normal instances. The results demonstrate that the proposed IDS effectively classifies cyberattacks relevant to robotic systems. The presented framework is also evaluated against existing IDS approaches in robotic systems. The results demonstrate that the proposed approach exhibits better results in terms of accuracy, robustness, and adaptability to emerging cyber threats. 2025 The Author(s). Published with license by Taylor &amp; Francis Group, LLC. Publisher An entity responsible for making the resource available Taylor and Francis Ltd. Relation A related resource ISSN: 8839514; CODEN: AAINE 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