A novel univariate feature selection with ANOVA F-test-based machine learning model for Intrusion Detection Framework of Robotics system
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 (...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2025.2539395 |
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| _version_ | 1849762136689999872 |
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| author | Narinder Verma Neerendra Kumar Kuljeet Singh Abeer Aljohani Anurag Sinha Syed Abid Hussain |
| author_facet | Narinder Verma Neerendra Kumar Kuljeet Singh Abeer Aljohani Anurag Sinha Syed Abid Hussain |
| author_sort | Narinder Verma |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-dd0430202f2a4dceada498a706c46faa |
| institution | DOAJ |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-dd0430202f2a4dceada498a706c46faa2025-08-20T03:05:49ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452025-12-0139110.1080/08839514.2025.2539395A novel univariate feature selection with ANOVA F-test-based machine learning model for Intrusion Detection Framework of Robotics systemNarinder Verma0Neerendra Kumar1Kuljeet Singh2Abeer Aljohani3Anurag Sinha4Syed Abid Hussain5Department of Computer Science and IT, Central University of Jammu, Jammu, IndiaDepartment of Computer Science and IT, Central University of Jammu, Jammu, IndiaDepartment of Computer Science, Christ University, Delhi, IndiaDepartment of Computer Science, Applied College, Taibah University, Medina, Kingdom of Saudi ArabiaTech School, Computer Science Department, ICFAI University, Ranchi, Jharkhand, IndiaDepartment of Computer Science and Engineering, Bakhtar University, Kabul, AfghanistanRobotic 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.https://www.tandfonline.com/doi/10.1080/08839514.2025.2539395 |
| spellingShingle | Narinder Verma Neerendra Kumar Kuljeet Singh Abeer Aljohani Anurag Sinha Syed Abid Hussain A novel univariate feature selection with ANOVA F-test-based machine learning model for Intrusion Detection Framework of Robotics system Applied Artificial Intelligence |
| title | A novel univariate feature selection with ANOVA F-test-based machine learning model for Intrusion Detection Framework of Robotics system |
| title_full | A novel univariate feature selection with ANOVA F-test-based machine learning model for Intrusion Detection Framework of Robotics system |
| title_fullStr | A novel univariate feature selection with ANOVA F-test-based machine learning model for Intrusion Detection Framework of Robotics system |
| title_full_unstemmed | A novel univariate feature selection with ANOVA F-test-based machine learning model for Intrusion Detection Framework of Robotics system |
| title_short | A novel univariate feature selection with ANOVA F-test-based machine learning model for Intrusion Detection Framework of Robotics system |
| title_sort | novel univariate feature selection with anova f test based machine learning model for intrusion detection framework of robotics system |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2025.2539395 |
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