Federated learning assisted IoT malware detection using static analysis
University of New Brunswick
The Internet of Things (IoT) has millions of connected devices; however, with the increased use of IoT devices, the threat of malware has increased rapidly. With technological advances, recent works have attempted to use Machine Learning and Deep Learning for IoT malware detection, but most of these techniques are centralized learning techniques, and they do not efficiently protect confidential user data. To address these issues in this research, we propose a Federated Learning-based approach that employs a Random Forest model for detecting IoT malware samples. Through the Federated Learning solution, we ensured that the local IoT device data remains locally and is not moved off the device. The results from our proposed model achieve an accuracy of 95% and efficiently classify the malware and benign samples. The overall comparative results between our proposed decentralized model and a centralized format demonstrate a significant improvement in the accuracy of malware detection while preserving the security of user data.