Combating IoT Attacks In AI-Driven Networks Via Robust And Resource-Efficient Federated Learning
Abstract
As Internet of things (IoT) networks expand and more real-time data are managed by Software Defined networking (SDN) controllers, the risk of cyber threats grows, making it crucial to ensure security and efficiency. Traditional Machine Learning approaches with centralized data processing struggle with the scalability needs of modern IoT systems. Federated Learning (FL) has emerged as a solution, enabling model training across devices without centralizing data, addressing privacy concerns and reducing server load. However, IoT networks face challenges like inconsistent data across devices, which can hinder FL's effectiveness in detecting IoT attacks. Additionally, the computing demands of model training can strain SDN controllers, increasing latency. This study addresses these challenges by (1) evaluating FL methods, including FedAvg, FedProx, and Scaffold, under varied data distributions to address statistical heterogeneity in detecting IoT attacks, and (2) proposing Resampling Dynamic Update Strategy or REDUS, a resampling technique that prioritizes challenging data while reducing redundancy, thereby lowering training time and latency. Experimental results show that REDUS achieves a 72.6% training time reduction with only 1.62% accuracy loss, providing an efficient solution for IoT attack detection in resource-constrained settings.