A Deep Learning Approach for Network Intrusion Detection Using a Small Features Vector
With the growth in network usage, there has been a corresponding growth in the nefarious exploitation of this technology. A wide array of techniques is now available that can be used to deal with cyberattacks, and one of them is network intrusion detection. Artificial Intelligence (AI) and Machine L...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Journal of Cybersecurity and Privacy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-800X/3/3/23 |
| _version_ | 1797579417336152064 |
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| author | Humera Ghani Bal Virdee Shahram Salekzamankhani |
| author_facet | Humera Ghani Bal Virdee Shahram Salekzamankhani |
| author_sort | Humera Ghani |
| collection | DOAJ |
| description | With the growth in network usage, there has been a corresponding growth in the nefarious exploitation of this technology. A wide array of techniques is now available that can be used to deal with cyberattacks, and one of them is network intrusion detection. Artificial Intelligence (AI) and Machine Learning (ML) techniques have extensively been employed to identify network anomalies. This paper provides an effective technique to evaluate the classification performance of a deep-learning-based Feedforward Neural Network (FFNN) classifier. A small feature vector is used to detect network traffic anomalies in the UNSW-NB15 and NSL-KDD datasets. The results show that a large feature set can have redundant and unuseful features, and it requires high computation power. The proposed technique exploits a small feature vector and achieves better classification accuracy. |
| first_indexed | 2024-03-10T22:35:48Z |
| format | Article |
| id | doaj.art-10c93748cf6749df914b1799be0895c9 |
| institution | Directory Open Access Journal |
| issn | 2624-800X |
| language | English |
| last_indexed | 2024-03-10T22:35:48Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Cybersecurity and Privacy |
| spelling | doaj.art-10c93748cf6749df914b1799be0895c92023-11-19T11:22:01ZengMDPI AGJournal of Cybersecurity and Privacy2624-800X2023-08-013345146310.3390/jcp3030023A Deep Learning Approach for Network Intrusion Detection Using a Small Features VectorHumera Ghani0Bal Virdee1Shahram Salekzamankhani2Centre for Communications Technology, School of Computing and Digital Media, London Metropolitan University, London N7 8DB, UKCentre for Communications Technology, School of Computing and Digital Media, London Metropolitan University, London N7 8DB, UKCentre for Communications Technology, School of Computing and Digital Media, London Metropolitan University, London N7 8DB, UKWith the growth in network usage, there has been a corresponding growth in the nefarious exploitation of this technology. A wide array of techniques is now available that can be used to deal with cyberattacks, and one of them is network intrusion detection. Artificial Intelligence (AI) and Machine Learning (ML) techniques have extensively been employed to identify network anomalies. This paper provides an effective technique to evaluate the classification performance of a deep-learning-based Feedforward Neural Network (FFNN) classifier. A small feature vector is used to detect network traffic anomalies in the UNSW-NB15 and NSL-KDD datasets. The results show that a large feature set can have redundant and unuseful features, and it requires high computation power. The proposed technique exploits a small feature vector and achieves better classification accuracy.https://www.mdpi.com/2624-800X/3/3/23deep learningfeedforward neural networknetwork intrusion detectionUNSW-NB15NSL-KDD |
| spellingShingle | Humera Ghani Bal Virdee Shahram Salekzamankhani A Deep Learning Approach for Network Intrusion Detection Using a Small Features Vector Journal of Cybersecurity and Privacy deep learning feedforward neural network network intrusion detection UNSW-NB15 NSL-KDD |
| title | A Deep Learning Approach for Network Intrusion Detection Using a Small Features Vector |
| title_full | A Deep Learning Approach for Network Intrusion Detection Using a Small Features Vector |
| title_fullStr | A Deep Learning Approach for Network Intrusion Detection Using a Small Features Vector |
| title_full_unstemmed | A Deep Learning Approach for Network Intrusion Detection Using a Small Features Vector |
| title_short | A Deep Learning Approach for Network Intrusion Detection Using a Small Features Vector |
| title_sort | deep learning approach for network intrusion detection using a small features vector |
| topic | deep learning feedforward neural network network intrusion detection UNSW-NB15 NSL-KDD |
| url | https://www.mdpi.com/2624-800X/3/3/23 |
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