Deep Learning Based DDoS Attack Detection
Nowadays, one of the biggest risks to network security is Distributed Denial of Service (DDoS) assaults, which cause disruptions to services by flooding systems with malicious traffic. Traditional approaches to detection, based on statistical thresholds and signature-based mechanisms, respectively,...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03005.pdf |
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| _version_ | 1825206599365427200 |
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| author | Xu Ziyi |
| author_facet | Xu Ziyi |
| author_sort | Xu Ziyi |
| collection | DOAJ |
| description | Nowadays, one of the biggest risks to network security is Distributed Denial of Service (DDoS) assaults, which cause disruptions to services by flooding systems with malicious traffic. Traditional approaches to detection, based on statistical thresholds and signature-based mechanisms, respectively, can hardly cope with the increasing complexity of such an attack. In order to improve detection accuracy and generalization, this research suggests a deep learning-based detection model that combines the Long Short-Term Memory (LSTM) network architecture with Convolutional Neural Networks (CNN). On the CICDDoS2019 dataset, which included several DDoS attack versions, the suggested model was trained and evaluated. The hybrid CNN-LSTM has extraction capabilities regarding both the spatial and temporal features of network traffic data, showing highly efficient performance. The classification resulting from this model yielded high accuracy with robust results for different attack scenarios. Results reflect the potential superiority of the given model in detecting DDoS attacks. Even though the performance was sound, the model still showed certain shortfalls, which were revealed when particular types of attacks were tested. Future work may be directed at further refining the model architecture, including optimizing diversity in training to allow for even better detection capabilities. |
| format | Article |
| id | doaj-art-d8fbe4a711fc4df0ae1039c23332e470 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-d8fbe4a711fc4df0ae1039c23332e4702025-02-07T08:21:11ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700300510.1051/itmconf/20257003005itmconf_dai2024_03005Deep Learning Based DDoS Attack DetectionXu Ziyi0Communication University of China, Hainan International CollegeNowadays, one of the biggest risks to network security is Distributed Denial of Service (DDoS) assaults, which cause disruptions to services by flooding systems with malicious traffic. Traditional approaches to detection, based on statistical thresholds and signature-based mechanisms, respectively, can hardly cope with the increasing complexity of such an attack. In order to improve detection accuracy and generalization, this research suggests a deep learning-based detection model that combines the Long Short-Term Memory (LSTM) network architecture with Convolutional Neural Networks (CNN). On the CICDDoS2019 dataset, which included several DDoS attack versions, the suggested model was trained and evaluated. The hybrid CNN-LSTM has extraction capabilities regarding both the spatial and temporal features of network traffic data, showing highly efficient performance. The classification resulting from this model yielded high accuracy with robust results for different attack scenarios. Results reflect the potential superiority of the given model in detecting DDoS attacks. Even though the performance was sound, the model still showed certain shortfalls, which were revealed when particular types of attacks were tested. Future work may be directed at further refining the model architecture, including optimizing diversity in training to allow for even better detection capabilities.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03005.pdf |
| spellingShingle | Xu Ziyi Deep Learning Based DDoS Attack Detection ITM Web of Conferences |
| title | Deep Learning Based DDoS Attack Detection |
| title_full | Deep Learning Based DDoS Attack Detection |
| title_fullStr | Deep Learning Based DDoS Attack Detection |
| title_full_unstemmed | Deep Learning Based DDoS Attack Detection |
| title_short | Deep Learning Based DDoS Attack Detection |
| title_sort | deep learning based ddos attack detection |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03005.pdf |
| work_keys_str_mv | AT xuziyi deeplearningbasedddosattackdetection |