CNID: Research of Network Intrusion Detection Based on Convolutional Neural Network
Network intrusion detection system can effectively detect network attack behaviour, which is very important to network security. In this paper, a multiclassification network intrusion detection model based on convolutional neural network is proposed, and the algorithm is optimized. First, the data i...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/4705982 |
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| _version_ | 1832566062331199488 |
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| author | Guojie Liu Jianbiao Zhang |
| author_facet | Guojie Liu Jianbiao Zhang |
| author_sort | Guojie Liu |
| collection | DOAJ |
| description | Network intrusion detection system can effectively detect network attack behaviour, which is very important to network security. In this paper, a multiclassification network intrusion detection model based on convolutional neural network is proposed, and the algorithm is optimized. First, the data is preprocessed, the original one-dimensional network intrusion data is converted into two-dimensional data, and then the effective features are learned using optimized convolutional neural networks, and, finally, the final test results are produced in conjunction with the Softmax classifier. In this paper, KDD-CUP 99 and NSL-KDD standard network intrusion detection dataset were used to carry out the multiclassification network intrusion detection experiment; the experimental results show that the multiclassification network intrusion detection model proposed in this paper improves the accuracy and check rate, reduces the false positive rate, and also obtains better test results for the detection of unknown attacks. |
| format | Article |
| id | doaj-art-53205e8eed044c0282b81cba33baa20c |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-53205e8eed044c0282b81cba33baa20c2025-02-03T01:05:17ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/47059824705982CNID: Research of Network Intrusion Detection Based on Convolutional Neural NetworkGuojie Liu0Jianbiao Zhang1Beijing University of Technology, Beijing 100124, ChinaBeijing University of Technology, Beijing 100124, ChinaNetwork intrusion detection system can effectively detect network attack behaviour, which is very important to network security. In this paper, a multiclassification network intrusion detection model based on convolutional neural network is proposed, and the algorithm is optimized. First, the data is preprocessed, the original one-dimensional network intrusion data is converted into two-dimensional data, and then the effective features are learned using optimized convolutional neural networks, and, finally, the final test results are produced in conjunction with the Softmax classifier. In this paper, KDD-CUP 99 and NSL-KDD standard network intrusion detection dataset were used to carry out the multiclassification network intrusion detection experiment; the experimental results show that the multiclassification network intrusion detection model proposed in this paper improves the accuracy and check rate, reduces the false positive rate, and also obtains better test results for the detection of unknown attacks.http://dx.doi.org/10.1155/2020/4705982 |
| spellingShingle | Guojie Liu Jianbiao Zhang CNID: Research of Network Intrusion Detection Based on Convolutional Neural Network Discrete Dynamics in Nature and Society |
| title | CNID: Research of Network Intrusion Detection Based on Convolutional Neural Network |
| title_full | CNID: Research of Network Intrusion Detection Based on Convolutional Neural Network |
| title_fullStr | CNID: Research of Network Intrusion Detection Based on Convolutional Neural Network |
| title_full_unstemmed | CNID: Research of Network Intrusion Detection Based on Convolutional Neural Network |
| title_short | CNID: Research of Network Intrusion Detection Based on Convolutional Neural Network |
| title_sort | cnid research of network intrusion detection based on convolutional neural network |
| url | http://dx.doi.org/10.1155/2020/4705982 |
| work_keys_str_mv | AT guojieliu cnidresearchofnetworkintrusiondetectionbasedonconvolutionalneuralnetwork AT jianbiaozhang cnidresearchofnetworkintrusiondetectionbasedonconvolutionalneuralnetwork |