| Title | Research on DDoS Attack Detection Method Based on Deep Neural Network Model inSDN |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Zhao, Wanqi, Sun, Haoyue, Zhang, Dawei |
| Conference Name | 2022 International Conference on Networking and Network Applications (NaNA) |
| Date Published | dec |
| Keywords | component, composability, Data models, DDoS attack detection, Deep Learning, Deep Neural Network, denial-of-service attack, feature extraction, Human Behavior, machine learning algorithms, Metrics, Neural networks, Predictive models, pubcrawl, resilience, Resiliency, software defined networking |
| Abstract | This paper studies Distributed Denial of Service (DDoS) attack detection by adopting the Deep Neural Network (DNN) model in Software Defined Networking (SDN). We first deploy the flow collector module to collect the flow table entries. Considering the detection efficiency of the DNN model, we also design some features manually in addition to the features automatically obtained by the flow table. Then we use the preprocessed data to train the DNN model and make a prediction. The overall detection framework is deployed in the SDN controller. The experiment results illustrate DNN model has higher accuracy in identifying attack traffic than machine learning algorithms, which lays a foundation for the defense against DDoS attack. |
| DOI | 10.1109/NaNA56854.2022.00038 |
| Citation Key | zhao_research_2022 |
Research on DDoS Attack Detection Method Based on Deep Neural Network Model inSDN