期刊
INFORMATION SCIENCES
卷 617, 期 -, 页码 133-149出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2022.10.060
关键词
IoMT-Blockchain; Healthcare system; Abnormal traffic detection; Multi -model feature; Feature fusion
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.
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
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