FL-SVM: A Federated Learning-Based Support Vector Machine Model for IoT Malware Detection
TL;DR
A more efficient IoT malware detection model based on an improved Federated Learning method that achieves good accuracy while strongly leveraging the advantages of Federated Learning in ensuring data privacy and minimizing computational resource usage during model training.
Abstract
The rapid development of IoT devices has significantly contributed to digital transformation across organizations, enterprises, and institutions. The risk of malware infection on IoT devices has become increasingly prevalent and dangerous, with new attack methods and infection techniques. IoT devices, with their numerous, diverse types, configurations and resource usage characteristics, have raised new requirements for more efficient, accurate IoT malware detection methods and solutions that ensure privacy during model training in real-world applications. In this paper, we propose a more efficient IoT malware detection model based on an improved Federated Learning method. Specifically, our key contributions include a dynamic aggregation mechanism designed for clients with heterogeneous feature spaces, allowing resource-constrained IoT devices to adaptively adjust their feature dimensionality according to hardware capacity. The proposed malware detection model has been tested with an IoT dataset on the MIPS architecture platform. Experimental results show that the proposed malware detection model achieves good accuracy while strongly leveraging the advantages of Federated Learning in ensuring data privacy and minimizing computational resource usage during model training.