Robust machine learning based Intrusion detection system using simple statistical techniques in feature selection
Abstract There are serious security issues with the quick growth of IoT devices, which are increasingly essential to Industry 4.0. These gadgets frequently function in challenging environments with little energy and processing power, leaving them open to cyberattacks and making it more difficult to...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88286-9 |
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| author | Sunil Kaushik Akashdeep Bhardwaj Ahmad Almogren Salil bharany Ayman Altameem Ateeq Ur Rehman Seada Hussen Habib Hamam |
| author_facet | Sunil Kaushik Akashdeep Bhardwaj Ahmad Almogren Salil bharany Ayman Altameem Ateeq Ur Rehman Seada Hussen Habib Hamam |
| author_sort | Sunil Kaushik |
| collection | DOAJ |
| description | Abstract There are serious security issues with the quick growth of IoT devices, which are increasingly essential to Industry 4.0. These gadgets frequently function in challenging environments with little energy and processing power, leaving them open to cyberattacks and making it more difficult to implement intrusion detection systems (IDS) that work. In order to address this issue, this study presents a unique feature selection algorithm based on basic statistical methods and a lightweight intrusion detection system. This methodology improves performance and cuts training time by 27–63% for a variety of classifiers. By utilizing the most discriminative features, the suggested methods lower the computational overhead and improve the detection accuracy. The IDS achieved over 99.9% accuracy, precision, recall, and F1-Score on the dataset IoTID20, with consistent performance on the NSLKDD dataset. |
| format | Article |
| id | doaj-art-321973dd907449a292c472fdf768f875 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-321973dd907449a292c472fdf768f8752025-02-02T12:17:54ZengNature PortfolioScientific Reports2045-23222025-02-0115112010.1038/s41598-025-88286-9Robust machine learning based Intrusion detection system using simple statistical techniques in feature selectionSunil Kaushik0Akashdeep Bhardwaj1Ahmad Almogren2Salil bharany3Ayman Altameem4Ateeq Ur Rehman5Seada Hussen6Habib Hamam7American Towers (ATC TIPL)Center of Excellence (Cybersecurity), School of Computer Science, UPESDepartment of Computer Science, College of Computer and Information Sciences, King Saud UniversityChitkara University Institute of Engineering and Technology, Chitkara UniversityDepartment of Natural and Engineering Sciences, College of Applied Studies and Community Services, King Saud UniversitySchool of Computing, Gachon UniversityDepartment of Electrical Power, Adama Science and Technology UniversityFaculty of Engineering, Uni de MonctonAbstract There are serious security issues with the quick growth of IoT devices, which are increasingly essential to Industry 4.0. These gadgets frequently function in challenging environments with little energy and processing power, leaving them open to cyberattacks and making it more difficult to implement intrusion detection systems (IDS) that work. In order to address this issue, this study presents a unique feature selection algorithm based on basic statistical methods and a lightweight intrusion detection system. This methodology improves performance and cuts training time by 27–63% for a variety of classifiers. By utilizing the most discriminative features, the suggested methods lower the computational overhead and improve the detection accuracy. The IDS achieved over 99.9% accuracy, precision, recall, and F1-Score on the dataset IoTID20, with consistent performance on the NSLKDD dataset.https://doi.org/10.1038/s41598-025-88286-9Statistical techniquesFeature selectionLightweight IDS |
| spellingShingle | Sunil Kaushik Akashdeep Bhardwaj Ahmad Almogren Salil bharany Ayman Altameem Ateeq Ur Rehman Seada Hussen Habib Hamam Robust machine learning based Intrusion detection system using simple statistical techniques in feature selection Scientific Reports Statistical techniques Feature selection Lightweight IDS |
| title | Robust machine learning based Intrusion detection system using simple statistical techniques in feature selection |
| title_full | Robust machine learning based Intrusion detection system using simple statistical techniques in feature selection |
| title_fullStr | Robust machine learning based Intrusion detection system using simple statistical techniques in feature selection |
| title_full_unstemmed | Robust machine learning based Intrusion detection system using simple statistical techniques in feature selection |
| title_short | Robust machine learning based Intrusion detection system using simple statistical techniques in feature selection |
| title_sort | robust machine learning based intrusion detection system using simple statistical techniques in feature selection |
| topic | Statistical techniques Feature selection Lightweight IDS |
| url | https://doi.org/10.1038/s41598-025-88286-9 |
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