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

【Author】 Wang, Jun; Jin, Hanlei; Chen, Junxiao; Tan, Jinghua; Zhong, Kaiyang

【Source】INFORMATION SCIENCES

【影响因子】8.233

【Abstract】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

【Keywords】IoMT-Blockchain; Healthcare system; Abnormal traffic detection; Multi -model feature; Feature fusion

【发表时间】2022 DEC

【收录时间】2022-11-28

【文献类型】实证数据

【主题类别】

区块链应用-实体经济-医疗领域