Research Article
IoT Malicious Traffic Detection Based on Federated Learning
@INPROCEEDINGS{10.1007/978-3-031-56580-9_15, author={Yi Shen and Yuhan Zhang and Yuwei Li and Wanmeng Ding and Miao Hu and Yang Li and Cheng Huang and Jie Wang}, title={IoT Malicious Traffic Detection Based on Federated Learning}, proceedings={Digital Forensics and Cyber Crime. 14th EAI International Conference, ICDF2C 2023, New York City, NY, USA, November 30, 2023, Proceedings, Part I}, proceedings_a={ICDF2C}, year={2024}, month={4}, keywords={Internet of Things Federated Learning Malicious Traffic}, doi={10.1007/978-3-031-56580-9_15} }- Yi Shen
Yuhan Zhang
Yuwei Li
Wanmeng Ding
Miao Hu
Yang Li
Cheng Huang
Jie Wang
Year: 2024
IoT Malicious Traffic Detection Based on Federated Learning
ICDF2C
Springer
DOI: 10.1007/978-3-031-56580-9_15
Abstract
Nowadays, a large number of IoT devices are manufactured and used in daily life. However, the lack of uniform protocols and standards for IoT devices brings many security risks. Malicious attacks on IoT devices such as Mirai are on the rise, leading to more IoT devices joining botnets and launching DDoS attacks. Therefore, it is necessary to detect malicious traffic of IoT devices. To solve this problem, we propose FLIMT, a federated learning based malicious traffic detection framework for IoT devices. We motivated by the fact that it is not practical to centralize and detect the traffic data sent by IoT devices. Besides, considering the data security and confidentiality standards, it is improper to aggregate data from individual IoT devices into a central computing cluster. FLIMT consists of several GRU-based local detection clients and a central server, where local clients rely on local data for model training and testing, and the central server for model aggregation. The experimental results show that FlIMT achieves high detection accuracy on real data collected from IoT devices, and significantly lessens communication rounds.
