A Customized Temporal Federated Learning Through Adversarial Networks for Cyber Attack Detection in IoT

The exponential growth of the Internet of Things (IoT) has heightened the need for secure, privacy-preserving, and efficient cyber-attack detection mechanisms. This study introduces the Customized Temporal Federated Learning through Adversarial Networks (CusTFL-AN) framework, which combines Temporal Convolutional Networks (TCNs) and Generative Adversarial Networks (GANs) for robust and personalized attack detection. CusTFL-AN enables clients to train local models while maintaining data privacy by generating synthetic datasets using GANs and aggregating these at a central server, thereby mitigating risks associated with direct data sharing. The framework's effectiveness is demonstrated on three benchmark datasets—UNSW-NB15, BoT-IoT, and Edge-IIoT—achieving detection accuracies of 99.2%, 99.5%, and 99.25%, respectively, significantly outperforming state-of-the-art methods. Key enhancements include addressing data heterogeneity through federated aggregation, minimizing overfitting using GAN validation and cross-validation techniques, and ensuring interpretability to support practical adoption in real-world IoT scenarios. Privacy mechanisms are strengthened to prevent potential data leakage during aggregation, and ethical considerations surrounding the use of synthetic datasets are acknowledged. Furthermore, the impact of computational constraints, network latency, and communication overhead on resource-constrained IoT devices has been carefully analyzed. While the results affirm the robustness and scalability of CusTFL-AN, future work will focus on extending evaluations to more diverse datasets and addressing the challenges of adversarial attacks. CusTFL-AN represents a significant step forward in privacy-preserving federated learning, offering practical solutions to real-world IoT cybersecurity challenges.