ItemArticle

A novel univariate feature selection with ANOVA F-test-based machine learning model for Intrusion Detection Framework of Robotics system

Title
A novel univariate feature selection with ANOVA F-test-based machine learning model for Intrusion Detection Framework of Robotics system
Creator
Verma, Narinder; Kumar, Neerendra; Singh, Kuljeet; Aljohani, Abeer; Sinha, Anurag; Hussain, Syed Abid
Description
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 & Francis Group, LLC.
Source
Applied Artificial Intelligence;Volume;39;Issue;1;Article No.;2539395;
Date
01-01-2025
Publisher
Taylor and Francis Ltd.
Coverage
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
Rights
All Open Access; Gold Open Access; Green Open Access
Relation
ISSN: 8839514; CODEN: AAINE
Format
online
Language
English
Type
Article

Citation

Verma, Narinder; Kumar, Neerendra; Singh, Kuljeet; Aljohani, Abeer; Sinha, Anurag; Hussain, Syed Abid, “A novel univariate feature selection with ANOVA F-test-based machine learning model for Intrusion Detection Framework of Robotics system,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/22665.

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