| dc.description.abstract |
Over the last one decade or so, Machine Learning has changed the global technology landscape with applications in almost all disciplines and verticals. Mobile and Web Security is an important research area in which researchers have been trying to apply Machine Learning, but data privacy concerns and high data communication costs to a central Machine Learning server have limited its use. Federated Learning is emerging as a promising solution which addresses privacy concerns and drastically reduces communication costs. In Federated Learning, data from individual devices is not communicated to a central server and model learning happens in a distributed manner. In this paper, we propose a Federated Learning solution for security of Android based devices. Mobile and Web Security solutions have evolved from signature-based detections to building Machine Learning models which are trained over large centralized malware repositories. We have used Federated Learning to learn security patterns from users' browsing data, which resides on individual devices and will never leave the devices. Federated Learning preserves users' privacy as it shares with the central server only the model that it learns from users' browsing data, and not the data itself. This way each mobile platform trains its own web security model from its data, and shares it to the centralized server. The centralized server aggregates these trained models received from numerous mobile devices and compiles an aggregated global model, which in turn is sent to mobile devices for inference. Mobile security solutions based on this concept create a sustained self-evolving security ecosystem, in which millions of mobile platforms share their learned models to form a robust distributed security paradigm. The results obtained using Federated Learning are found to be comparable with the results of centralized Machine Learning. |
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