A Fog-Based Threat Detection for Telemetry Smart Medical Devices Using a Real-Time and Lightweight Incremental Learning Method

Abstract

Smart telemetry medical devices do not have sufficient security measures, making them weak against different attacks. Machine learning (ML) has been broadly used for cyber-attack detection via on-gadgets and on-chip embedded models, which need to be held along with the medical devices, but with limited ability to perform heavy computations. The authors propose a real-time and lightweight fog computing-based threat detection using telemetry sensors data and their network traffic in NetFlow. The proposed method saves memory to a great extent as it does not require retraining. It is based on an incremental form of Hoeffding Tree Naïve Bayes Adaptive (HTNBA) and Incremental K-Nearest Neighbors (IKNN) algorithm. Furthermore, it matches the nature of sensor data which increases in seconds. Experimental results showed that the proposed model could detect different attacks against medical sensors with high accuracy (»100%), small memory usage (<50 MB), and low detection time in a few seconds.