Name
Title | ||
|---|---|---|
Collaborative DDoS Detection in Distributed Multi-Tenant IoT using Federated Learning |
Abstract | ||
|---|---|---|
Nowadays, the Internet of Things (IoT) has attracted much attention from the industry, and new initiatives are expected to be developed in the next decade. IoT is establishing a globally connected sensor network in which many devices are connected to the Internet generating large amounts of data. Conversely, many challenges need to be overcome to enable efficient and secure IoT applications (e.g., interoperability, security, standards, and server technologies). Furthermore, edge computing presents a paramount role in the diverse range of IoT applications. In this sense, processing sensitive data for different tenants (e.g., e-health and smart cities applications) requires transactions to be protected and isolated from different flows. Thereupon, different tenants can be targeted by Distributed Denial of Service (DDoS) attacks. However, attacks performed against a tenant remain unknown to others, preventing the improvement of detection and mitigation capabilities for DDoS attacks. The main obstacle in this collaboration relies on maintaining privacy in a multi-tenant environment while sharing the characteristics of attacks faced in the past. In this paper, we propose a collaborative DDoS detection and classification approach for distributed multi-tenant IoT environments using Federated Learning. This approach enables multiples tenants to collaboratively enhance their DDoS detection and classification capabilities across all edge nodes while maintaining their privacy. To accomplish this, tenants train deep learning instances on locally scaled traffic data and share the model parameters with other tenants. This strategy enables safer IoT operations and can be adopted in different applications. The experiments performed on a simulated environment considered the CICD-DoS2019 dataset and showed that the proposed approach can classify different DDoS attacks types with over 84.2% accuracy. The results demonstrate that collaborative DDoS detection enhances tenant protection compared to single detection. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/PST55820.2022.9851984 | 2022 19th Annual International Conference on Privacy, Security & Trust (PST) |
Keywords | DocType | ISSN |
Deep Learning,Federated Learning,Internet of Things (IoT),Distributed Denial of Service (DDoS),Privacy,Security | Conference | 1712-364X |
ISBN | Citations | PageRank |
978-1-6654-7399-6 | 0 | 0.34 |
References | Authors | |
33 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Euclides Carlos Pinto Neto | 1 | 0 | 0.34 |
| Sajjad Dadkhah | 2 | 1 | 0.71 |
| Ali A. Ghorbani | 3 | 1 | 0.71 |