Enhanced intrusion detection model based on principal component analysis and variable ensemble machine learning algorithm
The intrusion detection system (IDS) model, which can identify the presence of intruders in the network and take some predefined action for safe data transit across the network, is advantageous in achieving security in both simple and advanced network systems. Several IDS models have various securit...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier B.V.
2024
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/109022/1/FarkhanaMuchtar2024_EnhancedInstrusionDetectionModelBasedonPrincipal.pdf |
| _version_ | 1824452085659205632 |
|---|---|
| author | John, Ayuba Isnin, Ismail Fauzi Madni, Syed Hamid Hussain Muchtar, Farkhana |
| author_facet | John, Ayuba Isnin, Ismail Fauzi Madni, Syed Hamid Hussain Muchtar, Farkhana |
| author_sort | John, Ayuba |
| collection | ePrints |
| description | The intrusion detection system (IDS) model, which can identify the presence of intruders in the network and take some predefined action for safe data transit across the network, is advantageous in achieving security in both simple and advanced network systems. Several IDS models have various security problems, such as low detection accuracy and high false alarms, which can be caused by the network traffic dataset's excessive dimensionality and class imbalance in the creation of IDS models. Principal Component Analysis (PCA) has proven to be a helpful feature selection technique for dimensionality reduction. As a result, because it is a linear transformation, it has challenges capturing non-linear relationships between feature properties in the network traffic datasets. This paper proposes a variable ensemble machine learning method to solve the problem and achieve a low variance model with high accuracy and low false alarm. First, PCA is combined with the AdaBoost ensemble machine learning algorithm, which acts as stagewise additive modelling to compensate for PCA's deficiency in feature selection in network traffic by minimizing the exponential loss function. Secondly, PCA is used for feature selection, and a LogitBoost classifier algorithm can be used for multiclass classification and acts as an additive tree regression to compensate for the PCA's weakness by minimizing the Logistic Loss to provide an optimal classifier output. Finally, the low variance ability of RandomForest, which employs the bagging approach, is applied to eliminate overfittings. The experiments of the IDS model developed from the proposed methods were evaluated on the WSN-DS, NSL-KDD, and UNSW-N15 datasets. The performance of the methods, PCA with AdaBoost, on the WSN-DS dataset has an accuracy score of 92.3 %, an 89.0 % accuracy score on the NSL-KDD dataset, and a 67.9 % accuracy score on UNSW-N15, which is the least accurate score. PCA and RandomForest surpassed them by scoring 100 % accuracy on all three datasets. PCA and Bagging have an accuracy score of 99.8 % on the WSN-DS dataset, 100 % on the NSL-KDD dataset, and 93.4 % on the UNSW-N15 dataset. In comparison, PCA and LogitBoost have an accuracy score of 98.9 % on the WSN-DS dataset, 100 % on the NSL-KDD dataset, and 88.7 % on the UNSW-N15 dataset. |
| first_indexed | 2025-02-19T02:44:55Z |
| format | Article |
| id | utm.eprints-109022 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2025-02-19T02:44:55Z |
| publishDate | 2024 |
| publisher | Elsevier B.V. |
| record_format | dspace |
| spelling | utm.eprints-1090222025-01-27T08:36:52Z http://eprints.utm.my/109022/ Enhanced intrusion detection model based on principal component analysis and variable ensemble machine learning algorithm John, Ayuba Isnin, Ismail Fauzi Madni, Syed Hamid Hussain Muchtar, Farkhana T Technology (General) T58.6-58.62 Management information systems The intrusion detection system (IDS) model, which can identify the presence of intruders in the network and take some predefined action for safe data transit across the network, is advantageous in achieving security in both simple and advanced network systems. Several IDS models have various security problems, such as low detection accuracy and high false alarms, which can be caused by the network traffic dataset's excessive dimensionality and class imbalance in the creation of IDS models. Principal Component Analysis (PCA) has proven to be a helpful feature selection technique for dimensionality reduction. As a result, because it is a linear transformation, it has challenges capturing non-linear relationships between feature properties in the network traffic datasets. This paper proposes a variable ensemble machine learning method to solve the problem and achieve a low variance model with high accuracy and low false alarm. First, PCA is combined with the AdaBoost ensemble machine learning algorithm, which acts as stagewise additive modelling to compensate for PCA's deficiency in feature selection in network traffic by minimizing the exponential loss function. Secondly, PCA is used for feature selection, and a LogitBoost classifier algorithm can be used for multiclass classification and acts as an additive tree regression to compensate for the PCA's weakness by minimizing the Logistic Loss to provide an optimal classifier output. Finally, the low variance ability of RandomForest, which employs the bagging approach, is applied to eliminate overfittings. The experiments of the IDS model developed from the proposed methods were evaluated on the WSN-DS, NSL-KDD, and UNSW-N15 datasets. The performance of the methods, PCA with AdaBoost, on the WSN-DS dataset has an accuracy score of 92.3 %, an 89.0 % accuracy score on the NSL-KDD dataset, and a 67.9 % accuracy score on UNSW-N15, which is the least accurate score. PCA and RandomForest surpassed them by scoring 100 % accuracy on all three datasets. PCA and Bagging have an accuracy score of 99.8 % on the WSN-DS dataset, 100 % on the NSL-KDD dataset, and 93.4 % on the UNSW-N15 dataset. In comparison, PCA and LogitBoost have an accuracy score of 98.9 % on the WSN-DS dataset, 100 % on the NSL-KDD dataset, and 88.7 % on the UNSW-N15 dataset. Elsevier B.V. 2024-12 Article PeerReviewed application/pdf en http://eprints.utm.my/109022/1/FarkhanaMuchtar2024_EnhancedInstrusionDetectionModelBasedonPrincipal.pdf John, Ayuba and Isnin, Ismail Fauzi and Madni, Syed Hamid Hussain and Muchtar, Farkhana (2024) Enhanced intrusion detection model based on principal component analysis and variable ensemble machine learning algorithm. Intelligent Systems with Applications, 24 (NA). pp. 1-18. ISSN 2667-3053 http://dx.doi.org/10.1016/j.iswa.2024.200442 DOI:10.1016/j.iswa.2024.200442 |
| spellingShingle | T Technology (General) T58.6-58.62 Management information systems John, Ayuba Isnin, Ismail Fauzi Madni, Syed Hamid Hussain Muchtar, Farkhana Enhanced intrusion detection model based on principal component analysis and variable ensemble machine learning algorithm |
| title | Enhanced intrusion detection model based on principal component analysis and variable ensemble machine learning algorithm |
| title_full | Enhanced intrusion detection model based on principal component analysis and variable ensemble machine learning algorithm |
| title_fullStr | Enhanced intrusion detection model based on principal component analysis and variable ensemble machine learning algorithm |
| title_full_unstemmed | Enhanced intrusion detection model based on principal component analysis and variable ensemble machine learning algorithm |
| title_short | Enhanced intrusion detection model based on principal component analysis and variable ensemble machine learning algorithm |
| title_sort | enhanced intrusion detection model based on principal component analysis and variable ensemble machine learning algorithm |
| topic | T Technology (General) T58.6-58.62 Management information systems |
| url | http://eprints.utm.my/109022/1/FarkhanaMuchtar2024_EnhancedInstrusionDetectionModelBasedonPrincipal.pdf |
| work_keys_str_mv | AT johnayuba enhancedintrusiondetectionmodelbasedonprincipalcomponentanalysisandvariableensemblemachinelearningalgorithm AT isninismailfauzi enhancedintrusiondetectionmodelbasedonprincipalcomponentanalysisandvariableensemblemachinelearningalgorithm AT madnisyedhamidhussain enhancedintrusiondetectionmodelbasedonprincipalcomponentanalysisandvariableensemblemachinelearningalgorithm AT muchtarfarkhana enhancedintrusiondetectionmodelbasedonprincipalcomponentanalysisandvariableensemblemachinelearningalgorithm |