Increasing number of ubiquitous devices are being used in the medical field to collect patient information. Those connected sensors can potentially be exploited by third parties who want to misuse personal information and compromise the security, which could ultimately result even in patient death. This paper addresses the security concerns in eHealth networks and suggests a new approach to dealing with anomalies. In particular we propose a concept for safe in-hospital learning from internet of health things (IoHT) device data while securing the network traffic with a collaboratively trained anomaly detection system using federated learning. That way, real time traffic anomaly detection is achieved, while maintaining collaboration between hospitals and keeping local data secure and private. Since not only the network metadata, but also the actual medical data is relevant to anomaly detection, we propose to use differential privacy (DP) for providing formal guarantees of the privacy spending accumulated during the federated learning. CCS CONCEPTS• Security and privacy → Intrusion detection systems; • Applied computing → Health care information systems; • Computing methodologies → Anomaly detection; Distributed artificial intelligence.

No abstract

Given the Internet of Things rapid expansion and widespread adoption, it is of great concern to establish secure interaction between devices without worsening the quality of their performance. Using machine learning techniques has been shown to improve detecting anomalous behavior in these types of networks, but their implementation leads to poor performance and compromised privacy. To better address these shortcomings, federated learning is being introduced. It enables devices to collaboratively train and evaluate a shared model while keeping personal data on-site (e.g., smart homes, intensive care units, hospitals, etc.), thus minimizing the possibility of an attack and fostering real-time distribution of models and learning. The paper investigates the performance of federated learning in comparison to deep learning, with respect to network intrusion detection in ambient assisted living environments. The results demonstrate comparable performances of federated learning with deep learning, while achieving improved data privacy and security.

No abstract

No abstract