Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree
Cyber-attacks pose increasing challenges in precisely detecting intrusions, risking data confidentiality, integrity, and availability. This review paper presents recent IDS taxonomy, a comprehensive review of intrusion detection techniques, and commonly used datasets for evaluation. It discusses eva...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10185955/ |
| _version_ | 1797746143432540160 |
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| author | Zahedi Azam Md. Motaharul Islam Mohammad Nurul Huda |
| author_facet | Zahedi Azam Md. Motaharul Islam Mohammad Nurul Huda |
| author_sort | Zahedi Azam |
| collection | DOAJ |
| description | Cyber-attacks pose increasing challenges in precisely detecting intrusions, risking data confidentiality, integrity, and availability. This review paper presents recent IDS taxonomy, a comprehensive review of intrusion detection techniques, and commonly used datasets for evaluation. It discusses evasion techniques employed by attackers and the challenges in combating them to enhance network security. Researchers strive to improve IDS by accurately detecting intruders, reducing false positives, and identifying new threats. Machine learning (ML) and deep learning (DL) techniques are adopted in IDS systems, showing potential in efficiently detecting intruders across networks. The paper explores the latest trends and advancements in ML and DL-based network intrusion detection systems (NIDS), including methodology, evaluation metrics, and dataset selection. It emphasizes research obstacles and proposes a future research model to address weaknesses in the methodologies. The decision tree, known for its speed and user-friendliness, is proposed as a model for detecting result anomalies, combining findings from a comparative survey. This research aims to provide insights into building an effective decision tree-based detection framework. |
| first_indexed | 2024-03-12T15:32:49Z |
| format | Article |
| id | doaj.art-cc08d8da1f2b4f34a5476d90784b096b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T15:32:49Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cc08d8da1f2b4f34a5476d90784b096b2023-08-09T23:00:24ZengIEEEIEEE Access2169-35362023-01-0111803488039110.1109/ACCESS.2023.329644410185955Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision TreeZahedi Azam0https://orcid.org/0009-0000-7617-8030Md. Motaharul Islam1https://orcid.org/0000-0002-8030-3225Mohammad Nurul Huda2Department of Computer Science and Engineering, United International University, Dhaka, BangladeshDepartment of Computer Science and Engineering, United International University, Dhaka, BangladeshDepartment of Computer Science and Engineering, United International University, Dhaka, BangladeshCyber-attacks pose increasing challenges in precisely detecting intrusions, risking data confidentiality, integrity, and availability. This review paper presents recent IDS taxonomy, a comprehensive review of intrusion detection techniques, and commonly used datasets for evaluation. It discusses evasion techniques employed by attackers and the challenges in combating them to enhance network security. Researchers strive to improve IDS by accurately detecting intruders, reducing false positives, and identifying new threats. Machine learning (ML) and deep learning (DL) techniques are adopted in IDS systems, showing potential in efficiently detecting intruders across networks. The paper explores the latest trends and advancements in ML and DL-based network intrusion detection systems (NIDS), including methodology, evaluation metrics, and dataset selection. It emphasizes research obstacles and proposes a future research model to address weaknesses in the methodologies. The decision tree, known for its speed and user-friendliness, is proposed as a model for detecting result anomalies, combining findings from a comparative survey. This research aims to provide insights into building an effective decision tree-based detection framework.https://ieeexplore.ieee.org/document/10185955/Intrusion detection systemmachine learninginductive learningDDoS attacksdecision treesupervised and unsupervised learning |
| spellingShingle | Zahedi Azam Md. Motaharul Islam Mohammad Nurul Huda Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree IEEE Access Intrusion detection system machine learning inductive learning DDoS attacks decision tree supervised and unsupervised learning |
| title | Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree |
| title_full | Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree |
| title_fullStr | Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree |
| title_full_unstemmed | Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree |
| title_short | Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree |
| title_sort | comparative analysis of intrusion detection systems and machine learning based model analysis through decision tree |
| topic | Intrusion detection system machine learning inductive learning DDoS attacks decision tree supervised and unsupervised learning |
| url | https://ieeexplore.ieee.org/document/10185955/ |
| work_keys_str_mv | AT zahediazam comparativeanalysisofintrusiondetectionsystemsandmachinelearningbasedmodelanalysisthroughdecisiontree AT mdmotaharulislam comparativeanalysisofintrusiondetectionsystemsandmachinelearningbasedmodelanalysisthroughdecisiontree AT mohammadnurulhuda comparativeanalysisofintrusiondetectionsystemsandmachinelearningbasedmodelanalysisthroughdecisiontree |