Bayesian optimization based bloat prevention for secure IoT healthcare

Abstract

Health information technology is a subcategory of health technology that covers medical and healthcare information technology. It allows for the secure exchange of health information among consumers, providers, payers, and quality monitors, as well as the management of health information across computerized systems. In recent scenario, Internet of Medical Things (IoMT) collects the medical healthcare data via sensors, are further transmitted to remote servers, to be evaluated by the doctors for earlier disease detection. Conversely, there is always a threat on using wireless communication and the user’s private data can be targeted by the attackers. In this paper, Bayesian optimization-based bloat prevention for secure IoT healthcare, for identifying Attacks in secure healthcare system (BO-BLOAT). The gathered input datasets are pre-processed using the Natural Language Processing (NLP) techniques namely Sentence segmentation, Tokenization, Word Stemming and Removing stop words for removing irrelevant data. After preprocessing the features are extracted using RNN-BiLSTM and feature selection technique is done by Bayesian Optimization. The deep learning (DL) based Mobilenet network is utilized for attack detection. Finally, the classification and identifying the types of attack is performed by using DL based Ghost net. For performance analysis, the two dataset is utilized namely UNBDS-NB-15, KDD99. The classification results show that the proposed BO-BLOAT model attains higher rate of accuracy in attack detection than existing models. The proposed BO-BLOAT method has been simulated using MATLAB. The Proposed BO-BLOAT method improves the overall accuracy of the proposed BO-BLOAT, HFL, LRO-S, and GOL is 99.04%, 93.47%, 92.82% and 90.64% respectively.