A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning
The rapid growth of the Internet and communications has resulted in a huge increase in transmitted data. These data are coveted by attackers and they continuously create novel attacks to steal or corrupt these data. The growth of these attacks is an issue for the security of our systems and represen...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/22/11752 |
| _version_ | 1797465947925118976 |
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| author | Patrick Vanin Thomas Newe Lubna Luxmi Dhirani Eoin O’Connell Donna O’Shea Brian Lee Muzaffar Rao |
| author_facet | Patrick Vanin Thomas Newe Lubna Luxmi Dhirani Eoin O’Connell Donna O’Shea Brian Lee Muzaffar Rao |
| author_sort | Patrick Vanin |
| collection | DOAJ |
| description | The rapid growth of the Internet and communications has resulted in a huge increase in transmitted data. These data are coveted by attackers and they continuously create novel attacks to steal or corrupt these data. The growth of these attacks is an issue for the security of our systems and represents one of the biggest challenges for intrusion detection. An intrusion detection system (IDS) is a tool that helps to detect intrusions by inspecting the network traffic. Although many researchers have studied and created new IDS solutions, IDS still needs improving in order to have good detection accuracy while reducing false alarm rates. In addition, many IDS struggle to detect zero-day attacks. Recently, machine learning algorithms have become popular with researchers to detect network intrusion in an efficient manner and with high accuracy. This paper presents the concept of IDS and provides a taxonomy of machine learning methods. The main metrics used to assess an IDS are presented and a review of recent IDS using machine learning is provided where the strengths and weaknesses of each solution is outlined. Then, details of the different datasets used in the studies are provided and the accuracy of the results from the reviewed work is discussed. Finally, observations, research challenges and future trends are discussed. |
| first_indexed | 2024-03-09T18:29:49Z |
| format | Article |
| id | doaj.art-ec2eb5d165ba4213a37a78021d1fb9d2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T18:29:49Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ec2eb5d165ba4213a37a78021d1fb9d22023-11-24T07:40:21ZengMDPI AGApplied Sciences2076-34172022-11-0112221175210.3390/app122211752A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine LearningPatrick Vanin0Thomas Newe1Lubna Luxmi Dhirani2Eoin O’Connell3Donna O’Shea4Brian Lee5Muzaffar Rao6Department of Electronic and Computer Engineering, University of Limerick, V94 T9PX Limerick, IrelandDepartment of Electronic and Computer Engineering, University of Limerick, V94 T9PX Limerick, IrelandDepartment of Electronic and Computer Engineering, University of Limerick, V94 T9PX Limerick, IrelandDepartment of Electronic and Computer Engineering, University of Limerick, V94 T9PX Limerick, IrelandConfirm—SFI Centre for Smart Manufacturing, Park Point, Dublin Rd, Castletroy, V94 C928 Limerick, IrelandConfirm—SFI Centre for Smart Manufacturing, Park Point, Dublin Rd, Castletroy, V94 C928 Limerick, IrelandDepartment of Electronic and Computer Engineering, University of Limerick, V94 T9PX Limerick, IrelandThe rapid growth of the Internet and communications has resulted in a huge increase in transmitted data. These data are coveted by attackers and they continuously create novel attacks to steal or corrupt these data. The growth of these attacks is an issue for the security of our systems and represents one of the biggest challenges for intrusion detection. An intrusion detection system (IDS) is a tool that helps to detect intrusions by inspecting the network traffic. Although many researchers have studied and created new IDS solutions, IDS still needs improving in order to have good detection accuracy while reducing false alarm rates. In addition, many IDS struggle to detect zero-day attacks. Recently, machine learning algorithms have become popular with researchers to detect network intrusion in an efficient manner and with high accuracy. This paper presents the concept of IDS and provides a taxonomy of machine learning methods. The main metrics used to assess an IDS are presented and a review of recent IDS using machine learning is provided where the strengths and weaknesses of each solution is outlined. Then, details of the different datasets used in the studies are provided and the accuracy of the results from the reviewed work is discussed. Finally, observations, research challenges and future trends are discussed.https://www.mdpi.com/2076-3417/12/22/11752Intrusion Detection Systems (IDS)machine learningnetwork securityIntrusion Prevention Systems (IPS)deep learning algorithms |
| spellingShingle | Patrick Vanin Thomas Newe Lubna Luxmi Dhirani Eoin O’Connell Donna O’Shea Brian Lee Muzaffar Rao A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning Applied Sciences Intrusion Detection Systems (IDS) machine learning network security Intrusion Prevention Systems (IPS) deep learning algorithms |
| title | A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning |
| title_full | A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning |
| title_fullStr | A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning |
| title_full_unstemmed | A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning |
| title_short | A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning |
| title_sort | study of network intrusion detection systems using artificial intelligence machine learning |
| topic | Intrusion Detection Systems (IDS) machine learning network security Intrusion Prevention Systems (IPS) deep learning algorithms |
| url | https://www.mdpi.com/2076-3417/12/22/11752 |
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