Federated Learning Threat Classification with Heterogeneous Devices in Converged Energy Sector Networks
Open Access DepositedFrom Edge to Enterprise
Cyberattacks targeting the U.S. Energy Sector's converged networks areescalating in frequency and sophistication and present a significant risk that could cost the economy over $1 trillion in loses. The current threat landscape creates an urgent need for innovative, scalable, and adaptable threat detection systems that can operate across diverse hardware. To address this challenge, our research proposes and evaluates a Heterogeneous Federated Multi-layer Perceptron Threat Classification (HFMLP-TC) model. This approach utilizes a TensorFlow federated learning framework that uniquely incorporates computationally diverse clients, a high-performance i9 workstation and a resource constrained Raspberry Pi 5, for collaborative threat detection. Results demonstrate that the HFMLP-TC model significantly outperformed prior benchmark research, achieving a detection rate exceeding 99.9% and reducing the false negative rate to 0.03%. The results of our study lend credibility to the idea that integrating computationally constrained devices into real‑world cyber defense operations is not just a theoretical possibility but a practical one.
- All rights reserved
Notice to Authors
If you are the author of this work and you have any questions about the information on this page, please use the Contact form to get in touch with us.