CYBRIA - Pioneering Federated Learning for Privacy-Aware Cybersecurity with Brilliance Research study a federated learning framework for collaborative cyber threat detection without compromising confidential data. The decentralized approach trains models on local data distributed across clients and shares only intermediate model updates to generate an integrated global model.
If you use this dataset and code or any herein modified part of it in any publication, please cite these papers:
P. Thantharate and A. T, "CYBRIA - Pioneering Federated Learning for Privacy-Aware Cybersecurity with Brilliance," 2023 IEEE 20th International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET), Boca Raton, FL, USA, 2023, pp. 56-61, doi: 10.1109/HONET59747.2023.10374608.
Key Objectives
- Develop a federated learning framework called Cybria for collaborative cyber threat detection without compromising confidential data
- Evaluate model performance for intrusion detection using the Bot-IoT dataset
Proposed Solutions
- Designed a privacy-preserving federated learning architecture tailored for cybersecurity applications
- Implemented the Cybria model using TensorFlow Federated and Flower libraries
- Employed a decentralized approach where models are trained locally on clients and only model updates are shared
Simulated Results
- Cybria's federated model achieves 89.6% accuracy for intrusion detection compared to 81.4% for a centralized DNN
- The federated approach shows 8-10% better performance, demonstrating benefits of collaborative yet decentralized learning
- Local models allow specialized learning tuned to each client's data characteristics
Conclusion
- Preliminary results validate potential of federated learning to enhance cyber threat detection accuracy in a privacy-preserving manner
- Detailed studies needed to optimize model architectures, hyperparameters, and federation strategies for large real-world deployments
- Approach helps enable an ecosystem for collective security knowledge without increasing data centralization risks
References The implementation would follow the details provided in the original research paper: Thantharate and A. T, "CYBRIA - Pioneering Federated Learning for Privacy-Aware Cybersecurity with Brilliance," 2023 IEEE 20th International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET), Boca Raton, FL, USA, 2023, pp. 56-61, doi: 10.1109/HONET59747.2023.10374608.