Explainable Artificial Intelligence for Intrusion Detection System
Intrusion detection systems are widely utilized in the cyber security field, to prevent and mitigate threats. Intrusion detection systems (IDS) help to keep threats and vulnerabilities out of computer networks. To develop effective intrusion detection systems, a range of machine learning methods are...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/19/3079 |
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| author | Shruti Patil Vijayakumar Varadarajan Siddiqui Mohd Mazhar Abdulwodood Sahibzada Nihal Ahmed Onkar Sinha Satish Kumar Kailash Shaw Ketan Kotecha |
| author_facet | Shruti Patil Vijayakumar Varadarajan Siddiqui Mohd Mazhar Abdulwodood Sahibzada Nihal Ahmed Onkar Sinha Satish Kumar Kailash Shaw Ketan Kotecha |
| author_sort | Shruti Patil |
| collection | DOAJ |
| description | Intrusion detection systems are widely utilized in the cyber security field, to prevent and mitigate threats. Intrusion detection systems (IDS) help to keep threats and vulnerabilities out of computer networks. To develop effective intrusion detection systems, a range of machine learning methods are available. Machine learning ensemble methods have a well-proven track record when it comes to learning. Using ensemble methods of machine learning, this paper proposes an innovative intrusion detection system. To improve classification accuracy and eliminate false positives, features from the CICIDS-2017 dataset were chosen. This paper proposes an intrusion detection system using machine learning algorithms such as decision trees, random forests, and SVM (IDS). After training these models, an ensemble technique voting classifier was added and achieved an accuracy of 96.25%. Furthermore, the proposed model also incorporates the XAI algorithm LIME for better explainability and understanding of the black-box approach to reliable intrusion detection. Our experimental results confirmed that XAI LIME is more explanation-friendly and more responsive. |
| first_indexed | 2024-03-09T21:51:47Z |
| format | Article |
| id | doaj.art-56285acd78a5463a8d74a6c0f2fd082a |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T21:51:47Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-56285acd78a5463a8d74a6c0f2fd082a2023-11-23T20:05:55ZengMDPI AGElectronics2079-92922022-09-011119307910.3390/electronics11193079Explainable Artificial Intelligence for Intrusion Detection SystemShruti Patil0Vijayakumar Varadarajan1Siddiqui Mohd Mazhar2Abdulwodood Sahibzada3Nihal Ahmed4Onkar Sinha5Satish Kumar6Kailash Shaw7Ketan Kotecha8Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, IndiaSchool of Computer Science and Engineering, The University of New South Wales, Sydney, NSW 1466, AustraliaDepartment of Computer Science Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, IndiaDepartment of Computer Science Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, IndiaDepartment of Computer Science Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, IndiaDepartment of Computer Science Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, IndiaSymbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, IndiaDepartment of Computer Science Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, IndiaSymbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, IndiaIntrusion detection systems are widely utilized in the cyber security field, to prevent and mitigate threats. Intrusion detection systems (IDS) help to keep threats and vulnerabilities out of computer networks. To develop effective intrusion detection systems, a range of machine learning methods are available. Machine learning ensemble methods have a well-proven track record when it comes to learning. Using ensemble methods of machine learning, this paper proposes an innovative intrusion detection system. To improve classification accuracy and eliminate false positives, features from the CICIDS-2017 dataset were chosen. This paper proposes an intrusion detection system using machine learning algorithms such as decision trees, random forests, and SVM (IDS). After training these models, an ensemble technique voting classifier was added and achieved an accuracy of 96.25%. Furthermore, the proposed model also incorporates the XAI algorithm LIME for better explainability and understanding of the black-box approach to reliable intrusion detection. Our experimental results confirmed that XAI LIME is more explanation-friendly and more responsive.https://www.mdpi.com/2079-9292/11/19/3079IDSCICIDS2017XAILIMEensemble techniquesintrusion detection system |
| spellingShingle | Shruti Patil Vijayakumar Varadarajan Siddiqui Mohd Mazhar Abdulwodood Sahibzada Nihal Ahmed Onkar Sinha Satish Kumar Kailash Shaw Ketan Kotecha Explainable Artificial Intelligence for Intrusion Detection System Electronics IDS CICIDS2017 XAI LIME ensemble techniques intrusion detection system |
| title | Explainable Artificial Intelligence for Intrusion Detection System |
| title_full | Explainable Artificial Intelligence for Intrusion Detection System |
| title_fullStr | Explainable Artificial Intelligence for Intrusion Detection System |
| title_full_unstemmed | Explainable Artificial Intelligence for Intrusion Detection System |
| title_short | Explainable Artificial Intelligence for Intrusion Detection System |
| title_sort | explainable artificial intelligence for intrusion detection system |
| topic | IDS CICIDS2017 XAI LIME ensemble techniques intrusion detection system |
| url | https://www.mdpi.com/2079-9292/11/19/3079 |
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