A hybrid dimensionality reduction for network intrusion detection
Due to the wide variety of network services, many different types of protocols exist, producing various packet features. Some features contain irrelevant and redundant information. The presence of such features increases computational complexity and decreases accuracy. Therefore, this research is de...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI
2023
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| Online Access: | https://repository.londonmet.ac.uk/8888/1/published%20accepted%2010-11-2023%20jcp-03-00037.pdf |
| _version_ | 1804072952021385216 |
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| author | Ghani, Humera Salekzamankhani, Shahram Virdee, Bal Singh |
| author_facet | Ghani, Humera Salekzamankhani, Shahram Virdee, Bal Singh |
| author_sort | Ghani, Humera |
| collection | LMU |
| description | Due to the wide variety of network services, many different types of protocols exist, producing various packet features. Some features contain irrelevant and redundant information. The presence of such features increases computational complexity and decreases accuracy. Therefore, this research is designed to reduce the data dimensionality and improve the classification accuracy in the UNSW-NB15 dataset. It proposes a hybrid dimensionality reduction system that does feature selection (FS) and feature extraction (FE). FS was performed using the Recursive Feature Elimination (RFE) technique, while FE was accomplished by transforming the features into principal components. This combined scheme reduced a total of 41 input features into 15 components. The proposed systems’ classification performance was determined using an ensemble of Support Vector Classifier (SVC), K-nearest Neighbor classifier (KNC), and Deep Neural Network classifier (DNN). The system was evaluated using accuracy, detection rate, false positive rate, f1-score, and area under the curve metrics. Comparing the voting ensemble results of the full feature set against the 15 principal components confirms that reduced and transformed features did not significantly decrease the classifier’s performance. We achieved 94.34% accuracy, a 93.92% detection rate, a 5.23% false positive rate, a 94.32% f1-score, and a 94.34% area under the curve when 15 components were input to the voting ensemble classifier. |
| first_indexed | 2024-07-09T04:07:19Z |
| format | Article |
| id | oai:repository.londonmet.ac.uk:8888 |
| institution | London Metropolitan University |
| language | English |
| last_indexed | 2024-07-09T04:07:19Z |
| publishDate | 2023 |
| publisher | MDPI |
| record_format | eprints |
| spelling | oai:repository.londonmet.ac.uk:88882023-11-17T09:29:56Z http://repository.londonmet.ac.uk/8888/ A hybrid dimensionality reduction for network intrusion detection Ghani, Humera Salekzamankhani, Shahram Virdee, Bal Singh 000 Computer science, information & general works 600 Technology 620 Engineering & allied operations Due to the wide variety of network services, many different types of protocols exist, producing various packet features. Some features contain irrelevant and redundant information. The presence of such features increases computational complexity and decreases accuracy. Therefore, this research is designed to reduce the data dimensionality and improve the classification accuracy in the UNSW-NB15 dataset. It proposes a hybrid dimensionality reduction system that does feature selection (FS) and feature extraction (FE). FS was performed using the Recursive Feature Elimination (RFE) technique, while FE was accomplished by transforming the features into principal components. This combined scheme reduced a total of 41 input features into 15 components. The proposed systems’ classification performance was determined using an ensemble of Support Vector Classifier (SVC), K-nearest Neighbor classifier (KNC), and Deep Neural Network classifier (DNN). The system was evaluated using accuracy, detection rate, false positive rate, f1-score, and area under the curve metrics. Comparing the voting ensemble results of the full feature set against the 15 principal components confirms that reduced and transformed features did not significantly decrease the classifier’s performance. We achieved 94.34% accuracy, a 93.92% detection rate, a 5.23% false positive rate, a 94.32% f1-score, and a 94.34% area under the curve when 15 components were input to the voting ensemble classifier. MDPI 2023-11-16 Article PeerReviewed text en cc_by_4 https://repository.londonmet.ac.uk/8888/1/published%20accepted%2010-11-2023%20jcp-03-00037.pdf Ghani, Humera, Salekzamankhani, Shahram and Virdee, Bal Singh (2023) A hybrid dimensionality reduction for network intrusion detection. Journal of Cybersecurity and Privacy, 3 (4). pp. 830-843. ISSN 2624-800X https://www.mdpi.com/2624-800X/3/4/37 https://doi.org/10.3390/jcp3040037 |
| spellingShingle | 000 Computer science, information & general works 600 Technology 620 Engineering & allied operations Ghani, Humera Salekzamankhani, Shahram Virdee, Bal Singh A hybrid dimensionality reduction for network intrusion detection |
| title | A hybrid dimensionality reduction for network intrusion detection |
| title_full | A hybrid dimensionality reduction for network intrusion detection |
| title_fullStr | A hybrid dimensionality reduction for network intrusion detection |
| title_full_unstemmed | A hybrid dimensionality reduction for network intrusion detection |
| title_short | A hybrid dimensionality reduction for network intrusion detection |
| title_sort | hybrid dimensionality reduction for network intrusion detection |
| topic | 000 Computer science, information & general works 600 Technology 620 Engineering & allied operations |
| url | https://repository.londonmet.ac.uk/8888/1/published%20accepted%2010-11-2023%20jcp-03-00037.pdf |
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