A Novel Study: GAN-Based Minority Class Balancing and Machine-Learning-Based Network Intruder Detection Using Chi-Square Feature Selection
The network security problem becomes a routine problem for networks and cyber security specialists. The increased data on every minute not only creates big data problems, but also it expands the network size on the cloud and other computing technologies. Due to the big size and data, the network bec...
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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/2076-3417/12/22/11662 |
| _version_ | 1797465980069216256 |
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| author | Amerah Alabrah |
| author_facet | Amerah Alabrah |
| author_sort | Amerah Alabrah |
| collection | DOAJ |
| description | The network security problem becomes a routine problem for networks and cyber security specialists. The increased data on every minute not only creates big data problems, but also it expands the network size on the cloud and other computing technologies. Due to the big size and data, the network becomes more vulnerable to cyber-attacks. However, the detection of cyber-attacks on networks before or on time is a challenging task to solve. Therefore, the network intruder detection system (NIDS) is used to detect it. The network provided data-based NIDS were proposed previously, but still needed improvements. From the network data, it is also essential to find the most contributing features to avoid overfitting and lack of confidence in NIDS. The previously proposed solutions of NIDS mostly ignored the class imbalance problems that were normally found in the training of machine learning (ML) methods used in NIDS. However, few studies have tried to solve class imbalance and feature selection separately by achieving significant results on different datasets. The performance of these NIDS needs improvements in terms of classification and class balancing robust solutions. Therefore, to solve the class imbalance problem of minority classes in public datasets of NIDS and to select the most significant features, the proposed study gives a framework. In this framework, the minority class instances are generated using Generative Adversarial Network (GAN) model hyperparameter optimization and then the chi-square method of feature selection is applied to the fed six ML classifiers. The binary and multi-class classifications are applied on the UNSW-NB15 dataset with three versions of it. The comparative analysis on binary, multi-class classifications showed dominance as compared to previous studies in terms of accuracy (98.14%, 87.44%), precision (98.14%, 87.81%), F1-score (98.14%, 86.79%), Geometric-Mean (0.976, 0.923) and Area Under Cover (0.976, 0.94). |
| first_indexed | 2024-03-09T18:29:19Z |
| format | Article |
| id | doaj.art-64b383952b1f4c0aaed7e7324bd8f2de |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T18:29:19Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-64b383952b1f4c0aaed7e7324bd8f2de2023-11-24T07:38:54ZengMDPI AGApplied Sciences2076-34172022-11-0112221166210.3390/app122211662A Novel Study: GAN-Based Minority Class Balancing and Machine-Learning-Based Network Intruder Detection Using Chi-Square Feature SelectionAmerah Alabrah0Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaThe network security problem becomes a routine problem for networks and cyber security specialists. The increased data on every minute not only creates big data problems, but also it expands the network size on the cloud and other computing technologies. Due to the big size and data, the network becomes more vulnerable to cyber-attacks. However, the detection of cyber-attacks on networks before or on time is a challenging task to solve. Therefore, the network intruder detection system (NIDS) is used to detect it. The network provided data-based NIDS were proposed previously, but still needed improvements. From the network data, it is also essential to find the most contributing features to avoid overfitting and lack of confidence in NIDS. The previously proposed solutions of NIDS mostly ignored the class imbalance problems that were normally found in the training of machine learning (ML) methods used in NIDS. However, few studies have tried to solve class imbalance and feature selection separately by achieving significant results on different datasets. The performance of these NIDS needs improvements in terms of classification and class balancing robust solutions. Therefore, to solve the class imbalance problem of minority classes in public datasets of NIDS and to select the most significant features, the proposed study gives a framework. In this framework, the minority class instances are generated using Generative Adversarial Network (GAN) model hyperparameter optimization and then the chi-square method of feature selection is applied to the fed six ML classifiers. The binary and multi-class classifications are applied on the UNSW-NB15 dataset with three versions of it. The comparative analysis on binary, multi-class classifications showed dominance as compared to previous studies in terms of accuracy (98.14%, 87.44%), precision (98.14%, 87.81%), F1-score (98.14%, 86.79%), Geometric-Mean (0.976, 0.923) and Area Under Cover (0.976, 0.94).https://www.mdpi.com/2076-3417/12/22/11662chi-squareclass imbalancefeature selectionGANnetwork intruder detectionmachine learning |
| spellingShingle | Amerah Alabrah A Novel Study: GAN-Based Minority Class Balancing and Machine-Learning-Based Network Intruder Detection Using Chi-Square Feature Selection Applied Sciences chi-square class imbalance feature selection GAN network intruder detection machine learning |
| title | A Novel Study: GAN-Based Minority Class Balancing and Machine-Learning-Based Network Intruder Detection Using Chi-Square Feature Selection |
| title_full | A Novel Study: GAN-Based Minority Class Balancing and Machine-Learning-Based Network Intruder Detection Using Chi-Square Feature Selection |
| title_fullStr | A Novel Study: GAN-Based Minority Class Balancing and Machine-Learning-Based Network Intruder Detection Using Chi-Square Feature Selection |
| title_full_unstemmed | A Novel Study: GAN-Based Minority Class Balancing and Machine-Learning-Based Network Intruder Detection Using Chi-Square Feature Selection |
| title_short | A Novel Study: GAN-Based Minority Class Balancing and Machine-Learning-Based Network Intruder Detection Using Chi-Square Feature Selection |
| title_sort | novel study gan based minority class balancing and machine learning based network intruder detection using chi square feature selection |
| topic | chi-square class imbalance feature selection GAN network intruder detection machine learning |
| url | https://www.mdpi.com/2076-3417/12/22/11662 |
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