To what extent does the use of differential privacy in federated learning-based malware detection (as seen in

Abstract

Abstract: This work investigates the possibilities enabled by federated learning concerning IoT malware detection and studies security issues inherent to this new learning paradigm. In this context, a framework that uses federated learning to detect malware affecting IoT devices is presented. N-BaIoT, a dataset modeling network traffic of several real IoT devices while affected by malware, has been used to evaluate the proposed framework. Both supervised and unsupervised federated models (multi-layer perceptron and autoencoder) able to detect malware affecting seen and unseen IoT devices of N-BaIoT have

Research Question

To what extent does the use of differential privacy in federated learning-based malware detection (as seen in FEDetect) impact model generalization across different Android API versions, measured by F1-score comparisons on AndroZoo test subsets?

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