| Title | FLDDoS: DDoS Attack Detection Model based on Federated Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Zhang, Jiachao, Yu, Peiran, Qi, Le, Liu, Song, Zhang, Haiyu, Zhang, Jianzhong |
| Conference Name | 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) |
| Date Published | oct |
| Keywords | Clustering algorithms, Collaborative Work, Computational modeling, Data Imbalance, data privacy, DDoS Attack, Deep Learning, denial-of-service attack, federated learning, Human Behavior, human factors, Named Data Network Security, privacy, pubcrawl, resilience, Resiliency, Scalability |
| Abstract | Recently, DDoS attack has developed rapidly and become one of the most important threats to the Internet. Traditional machine learning and deep learning methods can-not train a satisfactory model based on the data of a single client. Moreover, in the real scenes, there are a large number of devices used for traffic collection, these devices often do not want to share data between each other depending on the research and analysis value of the attack traffic, which limits the accuracy of the model. Therefore, to solve these problems, we design a DDoS attack detection model based on federated learning named FLDDoS, so that the local model can learn the data of each client without sharing the data. In addition, considering that the distribution of attack detection datasets is extremely imbalanced and the proportion of attack samples is very small, we propose a hierarchical aggregation algorithm based on K-Means and a data resampling method based on SMOTEENN. The result shows that our model improves the accuracy by 4% compared with the traditional method, and reduces the number of communication rounds by 40%. |
| DOI | 10.1109/TrustCom53373.2021.00095 |
| Citation Key | zhang_flddos_2021 |
FLDDoS: DDoS Attack Detection Model based on Federated Learning