Implementation of art therapy assisted by the internet of medical things based on blockchain and fuzzy set theory

Abstract

This work aims to improve the stability and efficiency of the Internet of Medical Things (IoMT), strengthen the security and privacy of personal information, and create a healthy and safe network environment. First, art therapy in the current medical environment is discussed, and the security defenses of the IoMT supporting art therapy are studied. Then, a security defense method for IoMT is proposed by combining basic Blockchain knowledge with the Fuzzy Sets Theory. Specifically, a Density-awarded Fuzzy C-means (DFCM) algorithm is developed to solve the clustering issue of unbalanced data sets. Besides, the Fuzzy Support Vector Machine (FSVM) model is used to classify data points far from the sample center to increase classification accuracy. In the simulation experiment, the DFCM algorithm achieves a pleasing clustering effect on the four data sets, involving the data sets with density changes and imbalanced sample points within the cluster. Moreover, the combination of DFCM and FSVM (DFCM-FSVM) for attack detection in the Internet of Things (IoT) realizes the optimal accuracy in the detection of different behaviors compared with similar algorithms; the detection accuracy of normal behavior, scheduled attack, gray hole attack, and black hole attack is 99.8%, 89.8%, 99.4%, and 91.5%, respectively. This work creatively proposes a hybrid intrusion detection method based on fuzzy theory. As the second line of defense in security protection, it plays a critical role and is suitable for the network environment of the perception layer. In conclusion, the attack detection method based on Blockchain combined with the Fuzzy Sets Theory can precisely identify potential attacks in abnormal data, which has important practical value for maintaining data security and the regular operation of the IoMT.