Ensuring network security with a robust intrusion detection system using ensemble-based machine learning
Intrusion detection is a critical aspect of network security to protect computer systems from unauthorized access and attacks. The capacity of traditional intrusion detection systems (IDS) to identify unknown sophisticated threats is constrained by their reliance on signature-based detection. Approa...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-09-01
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| Series: | Array |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590005623000310 |
| _version_ | 1797680759062921216 |
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| author | Md. Alamgir Hossain Md. Saiful Islam |
| author_facet | Md. Alamgir Hossain Md. Saiful Islam |
| author_sort | Md. Alamgir Hossain |
| collection | DOAJ |
| description | Intrusion detection is a critical aspect of network security to protect computer systems from unauthorized access and attacks. The capacity of traditional intrusion detection systems (IDS) to identify unknown sophisticated threats is constrained by their reliance on signature-based detection. Approaches based on machine learning have shown promising results in identifying unknown malicious attacks. No learning algorithm-based model, however, is able to accurately and consistently detect all different kinds of attacks. Besides that, the existing models are tested for a specific dataset. In this research, a novel ensemble-based machine-learning technique for intrusion detection is presented. Numerous public datasets and multiple ensemble strategies, including Random Forest, Gradient Boosting, Adaboost, Gradient XGBoost, Bagging, and Simple Stacking, will be employed to evaluate the performance of the proposed approach. The most relevant features for the detection of intrusion are selected using correlation analysis, mutual information, and principal component analysis. Our research using different ensemble methods demonstrates that the proposed approach using the Random Forest technique outperforms existing approaches in terms of accuracy and FPR, typically exceeding 99% with better evaluation metrics like Precision, Recall, F1-score, Balanced Accuracy, Cohen's Kappa, etc. This strategy may be a useful tool for strengthening the safety of computer systems and networks against emerging cyber threats. |
| first_indexed | 2024-03-11T23:34:46Z |
| format | Article |
| id | doaj.art-5d6ee50f2d704c17bbf1a0006dcf556b |
| institution | Directory Open Access Journal |
| issn | 2590-0056 |
| language | English |
| last_indexed | 2024-03-11T23:34:46Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Array |
| spelling | doaj.art-5d6ee50f2d704c17bbf1a0006dcf556b2023-09-20T04:21:47ZengElsevierArray2590-00562023-09-0119100306Ensuring network security with a robust intrusion detection system using ensemble-based machine learningMd. Alamgir Hossain0Md. Saiful Islam1Corresponding author.; Institute of Information and Communication Technology (IICT), Bangladesh University of Engineering and Technology (BUET), Palashi, Dhaka, 1205, BangladeshInstitute of Information and Communication Technology (IICT), Bangladesh University of Engineering and Technology (BUET), Palashi, Dhaka, 1205, BangladeshIntrusion detection is a critical aspect of network security to protect computer systems from unauthorized access and attacks. The capacity of traditional intrusion detection systems (IDS) to identify unknown sophisticated threats is constrained by their reliance on signature-based detection. Approaches based on machine learning have shown promising results in identifying unknown malicious attacks. No learning algorithm-based model, however, is able to accurately and consistently detect all different kinds of attacks. Besides that, the existing models are tested for a specific dataset. In this research, a novel ensemble-based machine-learning technique for intrusion detection is presented. Numerous public datasets and multiple ensemble strategies, including Random Forest, Gradient Boosting, Adaboost, Gradient XGBoost, Bagging, and Simple Stacking, will be employed to evaluate the performance of the proposed approach. The most relevant features for the detection of intrusion are selected using correlation analysis, mutual information, and principal component analysis. Our research using different ensemble methods demonstrates that the proposed approach using the Random Forest technique outperforms existing approaches in terms of accuracy and FPR, typically exceeding 99% with better evaluation metrics like Precision, Recall, F1-score, Balanced Accuracy, Cohen's Kappa, etc. This strategy may be a useful tool for strengthening the safety of computer systems and networks against emerging cyber threats.http://www.sciencedirect.com/science/article/pii/S2590005623000310Intrusion detection systemFeature extraction for IDSEnsemble-based approachMachine learning for IDSComputer network securityCyber attacks detection |
| spellingShingle | Md. Alamgir Hossain Md. Saiful Islam Ensuring network security with a robust intrusion detection system using ensemble-based machine learning Array Intrusion detection system Feature extraction for IDS Ensemble-based approach Machine learning for IDS Computer network security Cyber attacks detection |
| title | Ensuring network security with a robust intrusion detection system using ensemble-based machine learning |
| title_full | Ensuring network security with a robust intrusion detection system using ensemble-based machine learning |
| title_fullStr | Ensuring network security with a robust intrusion detection system using ensemble-based machine learning |
| title_full_unstemmed | Ensuring network security with a robust intrusion detection system using ensemble-based machine learning |
| title_short | Ensuring network security with a robust intrusion detection system using ensemble-based machine learning |
| title_sort | ensuring network security with a robust intrusion detection system using ensemble based machine learning |
| topic | Intrusion detection system Feature extraction for IDS Ensemble-based approach Machine learning for IDS Computer network security Cyber attacks detection |
| url | http://www.sciencedirect.com/science/article/pii/S2590005623000310 |
| work_keys_str_mv | AT mdalamgirhossain ensuringnetworksecuritywitharobustintrusiondetectionsystemusingensemblebasedmachinelearning AT mdsaifulislam ensuringnetworksecuritywitharobustintrusiondetectionsystemusingensemblebasedmachinelearning |