| Title | Survey of DDoS Attack Detection Technology for Traceability |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Chen, Jing, Yang, Lei, Qiu, Ziqiao |
| Conference Name | 2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE) |
| Keywords | CNN, composability, Computer crime, DDoS Attack, DDoS attack detection, denial-of-service attack, detection, Economics, Human Behavior, machine learning, Metrics, Network security, pubcrawl, resilience, Resiliency, Traceability |
| Abstract | Target attack identification and detection has always been a concern of network security in the current environment. However, the economic losses caused by DDoS attacks are also enormous. In recent years, DDoS attack detection has made great progress mainly in the user application layer of the network layer. In this paper, a review and discussion are carried out according to the different detection methods and platforms. This paper mainly includes three parts, which respectively review statistics-based machine learning detection, target attack detection on SDN platform and attack detection on cloud service platform. Finally, the research suggestions for DDoS attack detection are given. |
| DOI | 10.1109/ECICE55674.2022.10042916 |
| Citation Key | chen_survey_2022 |
Survey of DDoS Attack Detection Technology for Traceability