Real-Time Anomaly Detection of Network Traffic Based on CNN
Network traffic anomaly detection mainly detects and analyzes abnormal traffic by extracting the statistical features of network traffic. It is necessary to fully understand the concept of symmetry in anomaly detection and anomaly mitigation. However, the original information on network traffic is e...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
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| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/15/6/1205 |
| _version_ | 1797592559949709312 |
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| author | Haitao Liu Haifeng Wang |
| author_facet | Haitao Liu Haifeng Wang |
| author_sort | Haitao Liu |
| collection | DOAJ |
| description | Network traffic anomaly detection mainly detects and analyzes abnormal traffic by extracting the statistical features of network traffic. It is necessary to fully understand the concept of symmetry in anomaly detection and anomaly mitigation. However, the original information on network traffic is easily lost, and the adjustment of dynamic network configuration becomes gradually complicated. To solve this problem, we designed and realized a new online anomaly detection system based on software defined networks. The system uses the convolutional neural network to directly extract the original features of the network flow for analysis, which can realize online real- time packet extraction and detection. It utilizes SDN to flexibly adapt to changes in the network, allowing for a zero-configuration anomaly detection system. The packet filter of the anomaly detection system is used to automatically implement mitigation strategies to achieve online real-time mitigation of abnormal traffic. The experimental results show that the proposed method is more accurate and can warn the network manager in time that security measures can be taken, which fully demonstrates that the method can effectively detect abnormal traffic problems and improve the security performance of edge clustering networks. |
| first_indexed | 2024-03-11T01:52:57Z |
| format | Article |
| id | doaj.art-477ce642c1da4b12b94f041d63afb245 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-11T01:52:57Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-477ce642c1da4b12b94f041d63afb2452023-11-18T12:50:55ZengMDPI AGSymmetry2073-89942023-06-01156120510.3390/sym15061205Real-Time Anomaly Detection of Network Traffic Based on CNNHaitao Liu0Haifeng Wang1Business School, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Information Science and Engineering, Linyi University, Linyi 276002, ChinaNetwork traffic anomaly detection mainly detects and analyzes abnormal traffic by extracting the statistical features of network traffic. It is necessary to fully understand the concept of symmetry in anomaly detection and anomaly mitigation. However, the original information on network traffic is easily lost, and the adjustment of dynamic network configuration becomes gradually complicated. To solve this problem, we designed and realized a new online anomaly detection system based on software defined networks. The system uses the convolutional neural network to directly extract the original features of the network flow for analysis, which can realize online real- time packet extraction and detection. It utilizes SDN to flexibly adapt to changes in the network, allowing for a zero-configuration anomaly detection system. The packet filter of the anomaly detection system is used to automatically implement mitigation strategies to achieve online real-time mitigation of abnormal traffic. The experimental results show that the proposed method is more accurate and can warn the network manager in time that security measures can be taken, which fully demonstrates that the method can effectively detect abnormal traffic problems and improve the security performance of edge clustering networks.https://www.mdpi.com/2073-8994/15/6/1205software defined networksconvolutional neural networksedge clustersanomaly detectionanomaly mitigation |
| spellingShingle | Haitao Liu Haifeng Wang Real-Time Anomaly Detection of Network Traffic Based on CNN Symmetry software defined networks convolutional neural networks edge clusters anomaly detection anomaly mitigation |
| title | Real-Time Anomaly Detection of Network Traffic Based on CNN |
| title_full | Real-Time Anomaly Detection of Network Traffic Based on CNN |
| title_fullStr | Real-Time Anomaly Detection of Network Traffic Based on CNN |
| title_full_unstemmed | Real-Time Anomaly Detection of Network Traffic Based on CNN |
| title_short | Real-Time Anomaly Detection of Network Traffic Based on CNN |
| title_sort | real time anomaly detection of network traffic based on cnn |
| topic | software defined networks convolutional neural networks edge clusters anomaly detection anomaly mitigation |
| url | https://www.mdpi.com/2073-8994/15/6/1205 |
| work_keys_str_mv | AT haitaoliu realtimeanomalydetectionofnetworktrafficbasedoncnn AT haifengwang realtimeanomalydetectionofnetworktrafficbasedoncnn |