Lightweight federated learning based detection of malicious activity in distributed networks

Abstract

In an increasingly complex cyber threat landscape, traditional malware detection methods often fall short, particularly within resource-limited distributed networks like smart grids. This research project aims to develop an efficient malware detection system for such distributed networks, focusing on three elements: feature extraction, feature selection, and classification. For classification, a lightweight and accurate machine-learning model needs to be developed.