Collabo: A Collaborative Machine Learning Model and Its Application to the Security of Heterogeneous Medical Data in an IoT Network

Abstract

With the increase in globalization, the degree of electronic communication increases. Such an increase is also experienced by many sectors including the medical sector, which communicates and generates large amounts of data related to the spread of diseases as observed in the case of the COVID-19 pandemic. This has led to the deployment of Internet of Things (IoT) networks in many medical centers. However, one main challenge is how to maintain the security of the data and devices in the network. In this study, we discuss the cybersecurity risk associated with IoT networks used for medical services and provide a solution for protecting medical data and devices using an agent-based approach. Unlike most conventional cybersecurity models that use agents based on deterministic logic or independent learning agents to detect and prevent cybersecurity attacks, we propose a cybersecurity model using a collaborative network of learning agents, called Collabo, that share both mutual and causal values regarding their actions on a common security target. Our experimental results demonstrate the significance of our model over conventional models.