An Effective Network Intrusion Detection System Using Recursive Feature Elimination Technique
Machine learning is an emerging area in research. Nowadays, researchers are utilizing machine learning across all domains to find optimal solutions. Machine learning facilitates the growth of an intrusion detection system (IDS) in the context of cyber security. These systems are proposed to identify...
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MDPI AG
2023-12-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/59/1/99 |
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| author | Narendra Singh Yadav Vijay Prakash Sharma D. Sikha Datta Reddy Saswati Mishra |
| author_facet | Narendra Singh Yadav Vijay Prakash Sharma D. Sikha Datta Reddy Saswati Mishra |
| author_sort | Narendra Singh Yadav |
| collection | DOAJ |
| description | Machine learning is an emerging area in research. Nowadays, researchers are utilizing machine learning across all domains to find optimal solutions. Machine learning facilitates the growth of an intrusion detection system (IDS) in the context of cyber security. These systems are proposed to identify and classify cyber-attacks on the network. However, an exhaustive assessment and performance evolution of various machine learning algorithms remains unavailable. In this study, we introduce a framework designed to nurture a versatile and efficient IDS adept at identifying and categorizing unexpected and evolving cyber threats. This is achieved through the use of Recursive Feature Elimination (RFE). In RFE, the algorithm is run recursively until a selected number of features are identified to enhance efficiency and reduce computational cost. The rapid detection of these attacks can facilitate the identification of potential intruders, and the damage will be lowered. We attained remarkable accuracies, with an average rate between 98% and 99% across all the classifiers and against all four types of attacks. The random forest and decision tree models stood out, each achieving peak accuracies of 99% in both KDD-99 and NSL-KDD Datasets. |
| format | Article |
| id | doaj-art-91c59e995f2e4a62b99a6eb2312f206e |
| institution | Kabale University |
| issn | 2673-4591 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-91c59e995f2e4a62b99a6eb2312f206e2025-08-20T03:43:22ZengMDPI AGEngineering Proceedings2673-45912023-12-015919910.3390/engproc2023059099An Effective Network Intrusion Detection System Using Recursive Feature Elimination TechniqueNarendra Singh Yadav0Vijay Prakash Sharma1D. Sikha Datta Reddy2Saswati Mishra3Department of Information Technology, Manipal University Jaipur, Jaipur 303007, Rajasthan, IndiaDepartment of Information Technology, Manipal University Jaipur, Jaipur 303007, Rajasthan, IndiaDepartment of Information Technology, Manipal University Jaipur, Jaipur 303007, Rajasthan, IndiaDepartment of Information Technology, Manipal University Jaipur, Jaipur 303007, Rajasthan, IndiaMachine learning is an emerging area in research. Nowadays, researchers are utilizing machine learning across all domains to find optimal solutions. Machine learning facilitates the growth of an intrusion detection system (IDS) in the context of cyber security. These systems are proposed to identify and classify cyber-attacks on the network. However, an exhaustive assessment and performance evolution of various machine learning algorithms remains unavailable. In this study, we introduce a framework designed to nurture a versatile and efficient IDS adept at identifying and categorizing unexpected and evolving cyber threats. This is achieved through the use of Recursive Feature Elimination (RFE). In RFE, the algorithm is run recursively until a selected number of features are identified to enhance efficiency and reduce computational cost. The rapid detection of these attacks can facilitate the identification of potential intruders, and the damage will be lowered. We attained remarkable accuracies, with an average rate between 98% and 99% across all the classifiers and against all four types of attacks. The random forest and decision tree models stood out, each achieving peak accuracies of 99% in both KDD-99 and NSL-KDD Datasets.https://www.mdpi.com/2673-4591/59/1/99intrusion detection systemna?ve BayesKNNrandom forestreverse feature elimination |
| spellingShingle | Narendra Singh Yadav Vijay Prakash Sharma D. Sikha Datta Reddy Saswati Mishra An Effective Network Intrusion Detection System Using Recursive Feature Elimination Technique Engineering Proceedings intrusion detection system na?ve Bayes KNN random forest reverse feature elimination |
| title | An Effective Network Intrusion Detection System Using Recursive Feature Elimination Technique |
| title_full | An Effective Network Intrusion Detection System Using Recursive Feature Elimination Technique |
| title_fullStr | An Effective Network Intrusion Detection System Using Recursive Feature Elimination Technique |
| title_full_unstemmed | An Effective Network Intrusion Detection System Using Recursive Feature Elimination Technique |
| title_short | An Effective Network Intrusion Detection System Using Recursive Feature Elimination Technique |
| title_sort | effective network intrusion detection system using recursive feature elimination technique |
| topic | intrusion detection system na?ve Bayes KNN random forest reverse feature elimination |
| url | https://www.mdpi.com/2673-4591/59/1/99 |
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