A Survey of CNN-Based Network Intrusion Detection
Over the past few years, Internet applications have become more advanced and widely used. This has increased the need for Internet networks to be secured. Intrusion detection systems (IDSs), which employ artificial intelligence (AI) methods, are vital to ensuring network security. As a branch of AI,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/16/8162 |
| _version_ | 1797411395356065792 |
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| author | Leila Mohammadpour Teck Chaw Ling Chee Sun Liew Alihossein Aryanfar |
| author_facet | Leila Mohammadpour Teck Chaw Ling Chee Sun Liew Alihossein Aryanfar |
| author_sort | Leila Mohammadpour |
| collection | DOAJ |
| description | Over the past few years, Internet applications have become more advanced and widely used. This has increased the need for Internet networks to be secured. Intrusion detection systems (IDSs), which employ artificial intelligence (AI) methods, are vital to ensuring network security. As a branch of AI, deep learning (DL) algorithms are now effectively applied in IDSs. Among deep learning neural networks, the convolutional neural network (CNN) is a well-known structure designed to process complex data. The CNN overcomes the typical limitations of conventional machine learning approaches and is mainly used in IDSs. Several CNN-based approaches are employed in IDSs to handle privacy issues and security threats. However, there are no comprehensive surveys of IDS schemes that have utilized CNN to the best of our knowledge. Hence, in this study, our primary focus is on CNN-based IDSs so as to increase our understanding of various uses of the CNN in detecting network intrusions, anomalies, and other types of attacks. This paper innovatively organizes the studied CNN-IDS approaches into multiple categories and describes their primary capabilities and contributions. The main features of these approaches, such as the dataset, architecture, input shape, evaluated metrics, performance, feature extraction, and classifier method, are compared. Because different datasets are used in CNN-IDS research, their experimental results are not comparable. Hence, this study also conducted an empirical experiment to compare different approaches based on standard datasets, and the comparative results are presented in detail. |
| first_indexed | 2024-03-09T04:44:29Z |
| format | Article |
| id | doaj.art-f7ab0ec178d44e48a324496e3bcb3aa8 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T04:44:29Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f7ab0ec178d44e48a324496e3bcb3aa82023-12-03T13:17:35ZengMDPI AGApplied Sciences2076-34172022-08-011216816210.3390/app12168162A Survey of CNN-Based Network Intrusion DetectionLeila Mohammadpour0Teck Chaw Ling1Chee Sun Liew2Alihossein Aryanfar3Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, MalaysiaDepartment of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, MalaysiaDepartment of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, MalaysiaDepartment of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, MalaysiaOver the past few years, Internet applications have become more advanced and widely used. This has increased the need for Internet networks to be secured. Intrusion detection systems (IDSs), which employ artificial intelligence (AI) methods, are vital to ensuring network security. As a branch of AI, deep learning (DL) algorithms are now effectively applied in IDSs. Among deep learning neural networks, the convolutional neural network (CNN) is a well-known structure designed to process complex data. The CNN overcomes the typical limitations of conventional machine learning approaches and is mainly used in IDSs. Several CNN-based approaches are employed in IDSs to handle privacy issues and security threats. However, there are no comprehensive surveys of IDS schemes that have utilized CNN to the best of our knowledge. Hence, in this study, our primary focus is on CNN-based IDSs so as to increase our understanding of various uses of the CNN in detecting network intrusions, anomalies, and other types of attacks. This paper innovatively organizes the studied CNN-IDS approaches into multiple categories and describes their primary capabilities and contributions. The main features of these approaches, such as the dataset, architecture, input shape, evaluated metrics, performance, feature extraction, and classifier method, are compared. Because different datasets are used in CNN-IDS research, their experimental results are not comparable. Hence, this study also conducted an empirical experiment to compare different approaches based on standard datasets, and the comparative results are presented in detail.https://www.mdpi.com/2076-3417/12/16/8162convolutional neural networkCNNnetwork securityintrusion detectiondeep learning |
| spellingShingle | Leila Mohammadpour Teck Chaw Ling Chee Sun Liew Alihossein Aryanfar A Survey of CNN-Based Network Intrusion Detection Applied Sciences convolutional neural network CNN network security intrusion detection deep learning |
| title | A Survey of CNN-Based Network Intrusion Detection |
| title_full | A Survey of CNN-Based Network Intrusion Detection |
| title_fullStr | A Survey of CNN-Based Network Intrusion Detection |
| title_full_unstemmed | A Survey of CNN-Based Network Intrusion Detection |
| title_short | A Survey of CNN-Based Network Intrusion Detection |
| title_sort | survey of cnn based network intrusion detection |
| topic | convolutional neural network CNN network security intrusion detection deep learning |
| url | https://www.mdpi.com/2076-3417/12/16/8162 |
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