Intrusion Detection Method of Multi-channel Autoencoder Deep Learning
Aiming at the shortcomings of the existing intrusion detection methods in detection accuracy and false alarm rate, an intrusion detection method of multi-channel autoencoder deep learning is proposed. The method is divided into two stages: unsupervised learning and supervised learning. Firstly, two...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2020-12-01
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| Series: | Jisuanji kexue yu tansuo |
| Subjects: | |
| Online Access: | http://fcst.ceaj.org/CN/abstract/abstract2485.shtml |
| _version_ | 1819084969815834624 |
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| author | YANG Jie, TANG Yachun, TAN Daojun, LIU Xiaobing |
| author_facet | YANG Jie, TANG Yachun, TAN Daojun, LIU Xiaobing |
| author_sort | YANG Jie, TANG Yachun, TAN Daojun, LIU Xiaobing |
| collection | DOAJ |
| description | Aiming at the shortcomings of the existing intrusion detection methods in detection accuracy and false alarm rate, an intrusion detection method of multi-channel autoencoder deep learning is proposed. The method is divided into two stages: unsupervised learning and supervised learning. Firstly, two independent autoencoders are trained by normal traffic and attack traffic respectively, and the two new feature vectors reconstructed and the original samples form a multi-channel eigenvector representation. Then, the 1-D convolutional neural network (CNN) is used to process the multi-channel eigenvector representation, and the possible dependence between channels is learned to better distinguish the difference between normal traffic and attack traffic. The proposed method combines unsupervised multi-channel feature learning and supervised cross-channel feature dependence learning to train a flexible and effective intrusion detection model, which greatly improves the accuracy of model detection. At the same time, in order to optimize the parameters of CNN and improve the identification effect of network on channel dependence, genetic algorithm is used to automatically find the optimal topology set of CNN model. The experimental results show that the proposed method achieves good results in multiple data sets and has better prediction accuracy than other intrusion detection algorithms. |
| first_indexed | 2024-12-21T20:56:55Z |
| format | Article |
| id | doaj.art-3daacf69734d4cc2a0b92dc6306925a2 |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-12-21T20:56:55Z |
| publishDate | 2020-12-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-3daacf69734d4cc2a0b92dc6306925a22022-12-21T18:50:33ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182020-12-0114122050206010.3778/j.issn.1673-9418.2007023Intrusion Detection Method of Multi-channel Autoencoder Deep LearningYANG Jie, TANG Yachun, TAN Daojun, LIU Xiaobing0School of Electronics and Information Engineering, Hunan University of Science and Engineering, Yongzhou, Hunan 425199, ChinaAiming at the shortcomings of the existing intrusion detection methods in detection accuracy and false alarm rate, an intrusion detection method of multi-channel autoencoder deep learning is proposed. The method is divided into two stages: unsupervised learning and supervised learning. Firstly, two independent autoencoders are trained by normal traffic and attack traffic respectively, and the two new feature vectors reconstructed and the original samples form a multi-channel eigenvector representation. Then, the 1-D convolutional neural network (CNN) is used to process the multi-channel eigenvector representation, and the possible dependence between channels is learned to better distinguish the difference between normal traffic and attack traffic. The proposed method combines unsupervised multi-channel feature learning and supervised cross-channel feature dependence learning to train a flexible and effective intrusion detection model, which greatly improves the accuracy of model detection. At the same time, in order to optimize the parameters of CNN and improve the identification effect of network on channel dependence, genetic algorithm is used to automatically find the optimal topology set of CNN model. The experimental results show that the proposed method achieves good results in multiple data sets and has better prediction accuracy than other intrusion detection algorithms.http://fcst.ceaj.org/CN/abstract/abstract2485.shtmlintrusion detectionautoencoderdeep learningmulti-channelgenetic algorithm (ga) |
| spellingShingle | YANG Jie, TANG Yachun, TAN Daojun, LIU Xiaobing Intrusion Detection Method of Multi-channel Autoencoder Deep Learning Jisuanji kexue yu tansuo intrusion detection autoencoder deep learning multi-channel genetic algorithm (ga) |
| title | Intrusion Detection Method of Multi-channel Autoencoder Deep Learning |
| title_full | Intrusion Detection Method of Multi-channel Autoencoder Deep Learning |
| title_fullStr | Intrusion Detection Method of Multi-channel Autoencoder Deep Learning |
| title_full_unstemmed | Intrusion Detection Method of Multi-channel Autoencoder Deep Learning |
| title_short | Intrusion Detection Method of Multi-channel Autoencoder Deep Learning |
| title_sort | intrusion detection method of multi channel autoencoder deep learning |
| topic | intrusion detection autoencoder deep learning multi-channel genetic algorithm (ga) |
| url | http://fcst.ceaj.org/CN/abstract/abstract2485.shtml |
| work_keys_str_mv | AT yangjietangyachuntandaojunliuxiaobing intrusiondetectionmethodofmultichannelautoencoderdeeplearning |