AN APPROACH HYBRID RECURRENT NEURAL NETWORK AND RULE-BASE FOR INTRUSION DETECTION SYSTEM
Network intrusion detection is one of the most important issues of network security and is a research interest of many researchers. In this paper, we present a model based on the combination of recurrent neural networks and rule sets for the network intrusion detection problem. The main idea of the...
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| Format: | Article |
| Language: | English |
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Dalat University
2019-06-01
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| Series: | Tạp chí Khoa học Đại học Đà Lạt |
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| Online Access: | http://tckh.dlu.edu.vn/index.php/tckhdhdl/article/view/544 |
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| _version_ | 1832573671421509632 |
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| author | Trần Thị Hương Phạm Văn Hạnh |
| author_facet | Trần Thị Hương Phạm Văn Hạnh |
| author_sort | Trần Thị Hương |
| collection | DOAJ |
| description | Network intrusion detection is one of the most important issues of network security and is a research interest of many researchers. In this paper, we present a model based on the combination of recurrent neural networks and rule sets for the network intrusion detection problem. The main idea of the model is to combine the strengths of each classification model. The rule set is capable of detecting known attacks, while the recurrent neural network has the advantage of detecting new attacks. A comparison of the detection efficiency of our model with the previous detection models on the same data set, KDD CUP 99, shows that the proposed model is effective for detecting network intrusions at rates higher than 99%. |
| format | Article |
| id | doaj-art-1210c32146cf4f69b8485bd7149a541d |
| institution | Kabale University |
| issn | 0866-787X 0866-787X |
| language | English |
| publishDate | 2019-06-01 |
| publisher | Dalat University |
| record_format | Article |
| series | Tạp chí Khoa học Đại học Đà Lạt |
| spelling | doaj-art-1210c32146cf4f69b8485bd7149a541d2025-02-02T03:11:29ZengDalat UniversityTạp chí Khoa học Đại học Đà Lạt0866-787X0866-787X2019-06-0192203310.37569/DalatUniversity.9.2.544(2019)276AN APPROACH HYBRID RECURRENT NEURAL NETWORK AND RULE-BASE FOR INTRUSION DETECTION SYSTEMTrần Thị Hương0Phạm Văn Hạnh1Khoa Toán - Cơ - Tin học, Trường Đại học Khoa học Tự nhiên, Đại học Quốc gia Hà NộiTrung tâm Tin học, Trường Đại học Luật Hà Nội, Hà Nội, Việt NamNetwork intrusion detection is one of the most important issues of network security and is a research interest of many researchers. In this paper, we present a model based on the combination of recurrent neural networks and rule sets for the network intrusion detection problem. The main idea of the model is to combine the strengths of each classification model. The rule set is capable of detecting known attacks, while the recurrent neural network has the advantage of detecting new attacks. A comparison of the detection efficiency of our model with the previous detection models on the same data set, KDD CUP 99, shows that the proposed model is effective for detecting network intrusions at rates higher than 99%.http://tckh.dlu.edu.vn/index.php/tckhdhdl/article/view/544hệ thống phát hiện xâm nhập mạngmạng nơ-ron truy hồitập luật. |
| spellingShingle | Trần Thị Hương Phạm Văn Hạnh AN APPROACH HYBRID RECURRENT NEURAL NETWORK AND RULE-BASE FOR INTRUSION DETECTION SYSTEM Tạp chí Khoa học Đại học Đà Lạt hệ thống phát hiện xâm nhập mạng mạng nơ-ron truy hồi tập luật. |
| title | AN APPROACH HYBRID RECURRENT NEURAL NETWORK AND RULE-BASE FOR INTRUSION DETECTION SYSTEM |
| title_full | AN APPROACH HYBRID RECURRENT NEURAL NETWORK AND RULE-BASE FOR INTRUSION DETECTION SYSTEM |
| title_fullStr | AN APPROACH HYBRID RECURRENT NEURAL NETWORK AND RULE-BASE FOR INTRUSION DETECTION SYSTEM |
| title_full_unstemmed | AN APPROACH HYBRID RECURRENT NEURAL NETWORK AND RULE-BASE FOR INTRUSION DETECTION SYSTEM |
| title_short | AN APPROACH HYBRID RECURRENT NEURAL NETWORK AND RULE-BASE FOR INTRUSION DETECTION SYSTEM |
| title_sort | approach hybrid recurrent neural network and rule base for intrusion detection system |
| topic | hệ thống phát hiện xâm nhập mạng mạng nơ-ron truy hồi tập luật. |
| url | http://tckh.dlu.edu.vn/index.php/tckhdhdl/article/view/544 |
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