Mitigating adversarial attacks in federated learning based network traffic classification applications using secure hierarchical remote attestation and adaptive aggregation framework

Background Federated learning (FL) enhances network traffic classification (NTC) with significant benefits in privacy, performance, and efficiency. However, the distributed nature of FL exposes NTC models to critical adversarial attacks from byzantine clients. These attacks, such as label flipping (LF) and model poisoning, can severely degrade overall model performance, while backdoor and generative adversarial network (GAN) based attacks can force the model to misclassify specific traffic classes. Securing FL-based NTC is paramount, as these vulnerabilities pose substantial threats to its vital role in network management, quality of service, and threat identification. Methods While various defensive measures for FL exist, they are often ineffective against multiple types of adversarial attacks, and their effectiveness diminishes as the number of attackers increases. To address this gap, this study proposed SHeRAA-FL, a secure framework for FL-based NTC. The framework secures the training process by combining remote attestation scoring, hierarchical training, and adaptive aggregation mechanisms, reinforced with hardware-level security and encrypted communication. We developed and evaluated SHeRAA-FL on public datasets, such as ISCX-VPN 2016 and N-BaIoT, benchmarking it against existing approaches, including weighted averaging, median-mean, trim-mean, Krum, and Multi-Krum. Results The evaluation results show that SHeRAA-FL effectively mitigates the impact of multiple types of adversarial attacks, even in scenarios with multiple attackers. For example, in the LF attack, other approaches recorded a 99.6% accuracy reduction, while SHeRAA-FL only recorded a 5.33% reduction. Moreover, in a normal scenario, the framework produces a model with the highest accuracy of 0.9130, indicating minimal disruption to the FL process.