Finding the Efficiency of ConvBi-LSTM Over Anticipation of Adversaries in WBANs

Introduction: Wireless Body Area Networks (WBANs) are similar to custom Wireless Sensor Networks, so these networks are prone to adversaries through their activities, but in healthcare applications, security is necessary for the patient data. Moreover, providing reliable healthcare to patients is essential, and for the right treatment, correct patient data is required. For this purpose, we need to eliminate anomalies and irrelevant data created by malicious persons, attackers, and unauthorized users. However, existing technologies are not able to detect adversaries and are unable to maintain the data for a long duration while transferring it. Aims: This research aims to identify adversarial attacks and solutions for these attacks to maintain reliable smart healthcare services Methodology: We proposed a Convolutional-Bi-directional Long Short-Term Memory (ConvBiLSTM) model that provides a solution for the detection of adversaries and robustness against adversaries. Bi-LSTM (Bidirectional-Long Short Term Memory), where the hyperparameters of BiLSTM are tuned using the PHMS (Prognosis Health Monitoring System) to detect malicious or irrelevant anomalies data. Result: Thus, the empirical outcomes of the proposed model showed that it accurately categorizes a patient's health status founded on abnormal vital signs and is useful for providing the proper medical care to the patients. Furthermore, the Convolution Neural Networks (CNN) performance is also evaluated spatially to examine the relationship between the sensor and CMS (Central Monitoring System) or doctor’s device. The accuracy, recall, precision, loss, time, and F1 score metrics are used for the performance evaluation of the proposed model. Conclusion: Besides, the proposed model performance is compared with the existing approaches using the MIMIC (Medical Information Mart for Intensive Care) data set.