The intrinsic convenience of federated learning in malware IoT detection

The Internet of Things is emerging as a key concept, defining a network of interconnected devices capable of seamless data collection, exchange, and analysis. However, due to their emphasis on simplicity, these devices are often vulnerable to malware attacks. This study examines the potential of machine learning methods, specifically in the context of Federated Learning, to enhance privacy protection and to benefit from IoT's decentralized nature, such as the low overhead traffic. The proposed approach is a federated machine learning algorithm based on a central aggregator and several clients. The study aims to conduct a comprehensive analysis using the IOT-23 dataset, which contains real and labeled instances of malware infections. The test outcomes demonstrate that the proposed approach outperforms centralized approaches regarding the global area under the precision-recall curve (AUPRC) and variance, with a significance level of 0.05.