| Title | A DDoS attack detection method based on deep learning two-level model CNN-LSTM in SDN network |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Li, Mengxue, Zhang, Binxin, Wang, Guangchang, ZhuGe, Bin, Jiang, Xian, Dong, Ligang |
| Conference Name | 2022 International Conference on Cloud Computing, Big Data Applications and Software Engineering (CBASE) |
| Keywords | Attack detection model, composability, Computer architecture, Computer crime, DDoS Attack, DDoS attack detection, Deep Learning, denial-of-service attack, feature extraction, Human Behavior, Metrics, pubcrawl, resilience, Resiliency, Stability analysis, telecommunication traffic |
| Abstract | This paper mainly explores the detection and defense of DDoS attacks in the SDN architecture of the 5G environment, and proposes a DDoS attack detection method based on the deep learning two-level model CNN-LSTM in the SDN network. Not only can it greatly improve the accuracy of attack detection, but it can also reduce the time for classifying and detecting network traffic, so that the transmission of DDoS attack traffic can be blocked in time to ensure the availability of network services. |
| DOI | 10.1109/CBASE57816.2022.00062 |
| Citation Key | li_ddos_2022 |
A DDoS attack detection method based on deep learning two-level model CNN-LSTM in SDN network