A DDoS attack detection method based on deep learning two-level model CNN-LSTM in SDN network
- Resource Type
- Conference
- Authors
- Li, Mengxue; Zhang, Binxin; Wang, Guangchang; ZhuGe, Bin; Jiang, Xian; Dong, Ligang
- Source
- 2022 International Conference on Cloud Computing, Big Data Applications and Software Engineering (CBASE) CBASE Cloud Computing, Big Data Applications and Software Engineering (CBASE), 2022 International Conference on. :282-287 Sep, 2022
- Subject
- Computing and Processing
Deep learning
Computer architecture
Telecommunication traffic
Denial-of-service attack
Feature extraction
Stability analysis
Computer crime
DDoS attack
Attack detection model
- Language
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.