Journal
SOICT 2019: PROCEEDINGS OF THE TENTH INTERNATIONAL SYMPOSIUM ON INFORMATION AND COMMUNICATION TECHNOLOGY
Volume -, Issue -, Pages 273-279Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3368926.3369705
Keywords
Network Anomaly Detection; Federated Learning; Deep Neural Networks
Categories
Funding
- Cooperative Education Project of China's Ministry of Education [201702098035]
Ask authors/readers for more resources
Because of the complexity of network traffic, there are various significant challenges in the network anomaly detection fields. One of the major challenges is the lack of labeled training data. In this paper, we use federated learning to tackle data scarcity problem and to preserve data privacy, where multiple participants collaboratively train a global model. Unlike the centralized training architecture, participants do not need to share their training to the server in federated learning, which can prevent the training data from being exploited by attackers. Moreover, most of the previous works focus on one specific task of anomaly detection, which restricts the application areas and can not provide more valuable information to network administrators. Therefore, we propose a multi-task deep neural network in federated learning (MT-DNN-FL) to perform network anomaly detection task, VPN (Tor) traffic recognition task, and traffic classification task, simultaneously. Compared with multiple single-task models, the multi-task method can reduce training time overhead. Experiments conducted on well-known CICIDS2017, ISCXVPN2016, and ISCXTor2016 datasets, show that the detection and classification performance achieved by the proposed method is better than the baseline methods in centralized training architecture.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available