Anomaly detection in Internet of medical Things with Blockchain from the perspective of deep neural network

IoMT technology has many advantages in healthcare system, such as optimizing the med-ical service model, improving the efficiency of hospital operation and management, and improving the overall service level of the hospital. IoMT devices do not have a security authentication mechanism, and the trust between devices relies heavily on centralized third-party services. Blockchain can provide a secure interactive environment for the med-ical Internet of Things. However, security issues in the IoMT-Blockchain environment are also becoming increasingly prominent. Cyber-attacks targeting IoMT-Blockchain will only compromise the security of IoT devices, but also seriously affect the security of the Internet. Therefore, how to detect abnormal traffic in the IoMT-Blockchain environment becomes particularly important. In this work, an abnormal traffic detection with deep neu-ral network is designed for abnormal traffic detection in IoMT-Blockchain environment. First, this work proposes a feature extraction algorithm based on multi-model autoen-coders. The algorithm processes the feature information in groups to reduce the complexity between traffic feature information. It builds a multi-model autoencoder to further extract fusion features between multi-model features. Second, to maximize use of traffic data information in detection network, this work proposes a multi-feature sequence anomaly detection algorithm. The algorithm extracts low-level fusion features and high-level tem-poral features in network traffic respectively, and applies the features to anomaly detection and classification tasks by means of residual learning.(c) 2022 Published by Elsevier

Automated summary

IoMT technology has advantages in healthcare system, but security issues in the IoMT-Blockchain environment are prominent. This work proposes an abnormal traffic detection method using deep neural network for IoMT-Blockchain environment, which includes feature extraction and multi-feature sequence anomaly detection algorithms.