TGA: A Novel Network Intrusion Detection Method Based on TCN, BiGRU and Attention Mechanism
With the increasing complexity of the network environment, the types of network attacks are gradually increasing. Network intrusion detection systems can detect and identify network attacks effectively. However, the existing methods have some limitations, focusing only on local or global temporal fe...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/13/2849 |
| _version_ | 1797591828212482048 |
|---|---|
| author | Yangyang Song Nurbol Luktarhan Zhaolei Shi Haojie Wu |
| author_facet | Yangyang Song Nurbol Luktarhan Zhaolei Shi Haojie Wu |
| author_sort | Yangyang Song |
| collection | DOAJ |
| description | With the increasing complexity of the network environment, the types of network attacks are gradually increasing. Network intrusion detection systems can detect and identify network attacks effectively. However, the existing methods have some limitations, focusing only on local or global temporal features of network traffic. To address the above issues, we present a novel network intrusion detection model (TGA) based on Temporal Convolutional Network (TCN), Bidirectional Gated Recurrent Unit (BiGRU), and self-attention mechanism. TCN extracts local temporal information from network traffic sequences, while BiGRU extracts global temporal information from network traffic sequences. However, TCN and BiGRU do not consider the weights of features when extracting them, so an attention mechanism is added. The feature vectors obtained in TCN and BiGRU are fused and then input into the self-attention mechanism to capture the correlation between different positions in the sequence and reassign the weights of the temporal features to further enhance the model’s capabilities. Lastly, it is delivered to the classifier to classify different network traffic classes. Our method achieves 97.83% accuracy on the public CSE-CIC-IDS2018 dataset. After extensive experiments, our idea proved to be reasonable and practical. |
| first_indexed | 2024-03-11T01:43:57Z |
| format | Article |
| id | doaj.art-568669a168ba4a0b97e983b47310e669 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T01:43:57Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-568669a168ba4a0b97e983b47310e6692023-11-18T16:24:19ZengMDPI AGElectronics2079-92922023-06-011213284910.3390/electronics12132849TGA: A Novel Network Intrusion Detection Method Based on TCN, BiGRU and Attention MechanismYangyang Song0Nurbol Luktarhan1Zhaolei Shi2Haojie Wu3College of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaSchool of Software, Xinjiang University, Urumqi 830046, ChinaWith the increasing complexity of the network environment, the types of network attacks are gradually increasing. Network intrusion detection systems can detect and identify network attacks effectively. However, the existing methods have some limitations, focusing only on local or global temporal features of network traffic. To address the above issues, we present a novel network intrusion detection model (TGA) based on Temporal Convolutional Network (TCN), Bidirectional Gated Recurrent Unit (BiGRU), and self-attention mechanism. TCN extracts local temporal information from network traffic sequences, while BiGRU extracts global temporal information from network traffic sequences. However, TCN and BiGRU do not consider the weights of features when extracting them, so an attention mechanism is added. The feature vectors obtained in TCN and BiGRU are fused and then input into the self-attention mechanism to capture the correlation between different positions in the sequence and reassign the weights of the temporal features to further enhance the model’s capabilities. Lastly, it is delivered to the classifier to classify different network traffic classes. Our method achieves 97.83% accuracy on the public CSE-CIC-IDS2018 dataset. After extensive experiments, our idea proved to be reasonable and practical.https://www.mdpi.com/2079-9292/12/13/2849network intrusion detectiontemporal convolutional networkbidirectional gated recurrent unitself-attention mechanism |
| spellingShingle | Yangyang Song Nurbol Luktarhan Zhaolei Shi Haojie Wu TGA: A Novel Network Intrusion Detection Method Based on TCN, BiGRU and Attention Mechanism Electronics network intrusion detection temporal convolutional network bidirectional gated recurrent unit self-attention mechanism |
| title | TGA: A Novel Network Intrusion Detection Method Based on TCN, BiGRU and Attention Mechanism |
| title_full | TGA: A Novel Network Intrusion Detection Method Based on TCN, BiGRU and Attention Mechanism |
| title_fullStr | TGA: A Novel Network Intrusion Detection Method Based on TCN, BiGRU and Attention Mechanism |
| title_full_unstemmed | TGA: A Novel Network Intrusion Detection Method Based on TCN, BiGRU and Attention Mechanism |
| title_short | TGA: A Novel Network Intrusion Detection Method Based on TCN, BiGRU and Attention Mechanism |
| title_sort | tga a novel network intrusion detection method based on tcn bigru and attention mechanism |
| topic | network intrusion detection temporal convolutional network bidirectional gated recurrent unit self-attention mechanism |
| url | https://www.mdpi.com/2079-9292/12/13/2849 |
| work_keys_str_mv | AT yangyangsong tgaanovelnetworkintrusiondetectionmethodbasedontcnbigruandattentionmechanism AT nurbolluktarhan tgaanovelnetworkintrusiondetectionmethodbasedontcnbigruandattentionmechanism AT zhaoleishi tgaanovelnetworkintrusiondetectionmethodbasedontcnbigruandattentionmechanism AT haojiewu tgaanovelnetworkintrusiondetectionmethodbasedontcnbigruandattentionmechanism |