An Analysis of Intrusion Detection Classification using Supervised Machine Learning Algorithms on NSL-KDD Dataset
From the past few years, Intrusion Detection Systems (IDS) are employed as a second line of defence and have shown to be a useful tool for enhancing security by detecting suspicious activity. Anomaly based intrusion detection is a type of intrusion detection system that identifies anomalies. Conven...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis
2022-03-01
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| Series: | Journal of Computing Research and Innovation |
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| Online Access: | https://crinn.conferencehunter.com/index.php/jcrinn/article/view/274 |
| _version_ | 1797834439177273344 |
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| author | Sarthak Rastogi Archit Shrotriya Mitul Kumar Singh Raghu Vamsi Potukuchi |
| author_facet | Sarthak Rastogi Archit Shrotriya Mitul Kumar Singh Raghu Vamsi Potukuchi |
| author_sort | Sarthak Rastogi |
| collection | DOAJ |
| description |
From the past few years, Intrusion Detection Systems (IDS) are employed as a second line of defence and have shown to be a useful tool for enhancing security by detecting suspicious activity. Anomaly based intrusion detection is a type of intrusion detection system that identifies anomalies. Conventional IDS are less accurate in detecting anomalies because of the decision taking based on rules. The IDS with machine learning method improves the detection accuracy of the security attacks. To this end, this paper studies the classification analysis of intrusion detection using various supervised learning algorithms such as SVM, Naive Bayes, KNN, Random Forest, Logistic Regression and Decision tree on the NSL-KDD dataset. The findings reveal which method performed better in terms of accuracy and running time.
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| first_indexed | 2024-04-09T14:37:58Z |
| format | Article |
| id | doaj.art-66a4323d05b94f12ba9f19bec5a8feb6 |
| institution | Directory Open Access Journal |
| issn | 2600-8793 |
| language | English |
| last_indexed | 2024-04-09T14:37:58Z |
| publishDate | 2022-03-01 |
| publisher | Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis |
| record_format | Article |
| series | Journal of Computing Research and Innovation |
| spelling | doaj.art-66a4323d05b94f12ba9f19bec5a8feb62023-05-03T10:36:45ZengFaculty of Computer and Mathematical Sciences, Universiti Teknologi MARA PerlisJournal of Computing Research and Innovation2600-87932022-03-017110.24191/jcrinn.v7i1.274274An Analysis of Intrusion Detection Classification using Supervised Machine Learning Algorithms on NSL-KDD DatasetSarthak Rastogi0Archit Shrotriya1Mitul Kumar Singh2Raghu Vamsi Potukuchi3Department of CSE, Jaypee Institute of Information Technology, Sector 62, NOIDA, IndiaDepartment of CSE, Jaypee Institute of Information Technology, Sector 62, NOIDA, IndiaDepartment of CSE, Jaypee Institute of Information Technology, Sector 62, NOIDA, IndiaDepartment of CSE, Jaypee Institute of Information Technology, Sector 62, NOIDA, India From the past few years, Intrusion Detection Systems (IDS) are employed as a second line of defence and have shown to be a useful tool for enhancing security by detecting suspicious activity. Anomaly based intrusion detection is a type of intrusion detection system that identifies anomalies. Conventional IDS are less accurate in detecting anomalies because of the decision taking based on rules. The IDS with machine learning method improves the detection accuracy of the security attacks. To this end, this paper studies the classification analysis of intrusion detection using various supervised learning algorithms such as SVM, Naive Bayes, KNN, Random Forest, Logistic Regression and Decision tree on the NSL-KDD dataset. The findings reveal which method performed better in terms of accuracy and running time. https://crinn.conferencehunter.com/index.php/jcrinn/article/view/274NSL-KDDIntrusion Detection SystemMachine LearningAnomalySVMKNN |
| spellingShingle | Sarthak Rastogi Archit Shrotriya Mitul Kumar Singh Raghu Vamsi Potukuchi An Analysis of Intrusion Detection Classification using Supervised Machine Learning Algorithms on NSL-KDD Dataset Journal of Computing Research and Innovation NSL-KDD Intrusion Detection System Machine Learning Anomaly SVM KNN |
| title | An Analysis of Intrusion Detection Classification using Supervised Machine Learning Algorithms on NSL-KDD Dataset |
| title_full | An Analysis of Intrusion Detection Classification using Supervised Machine Learning Algorithms on NSL-KDD Dataset |
| title_fullStr | An Analysis of Intrusion Detection Classification using Supervised Machine Learning Algorithms on NSL-KDD Dataset |
| title_full_unstemmed | An Analysis of Intrusion Detection Classification using Supervised Machine Learning Algorithms on NSL-KDD Dataset |
| title_short | An Analysis of Intrusion Detection Classification using Supervised Machine Learning Algorithms on NSL-KDD Dataset |
| title_sort | analysis of intrusion detection classification using supervised machine learning algorithms on nsl kdd dataset |
| topic | NSL-KDD Intrusion Detection System Machine Learning Anomaly SVM KNN |
| url | https://crinn.conferencehunter.com/index.php/jcrinn/article/view/274 |
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