###### Abstract  
Deep learning (DL) models are frequently employed to extract valuable features from heterogeneous and high-dimensional healthcare data, which are used to keep track of patient well-being via healthcare monitoring systems. Essentially, the training and testing data for such models are collected by huge IoT devices that may contain noise (e.g., incorrect labels, abnormal data, and incomplete information) and may be subject to various types of adversarial attacks. Therefore, to ensure the reliability of the various Internet of Healthcare Things (IoHT) applications, the training and testing data that are required for such DL techniques should be guaranteed to be clean. This paper proposes a hybrid convolutional long short-term memory (ConvLSTM) technique to assure the reliability of IoHT monitoring applications by detecting anomalies and adversarial content in the training data used for developing DL models. Furthermore, countermeasure techniques are suggested to protect the DL models against such adversarial attacks during the training phase. An experimental evaluation using the public PhysioNet dataset demonstrates the ability of the proposed model to detect anomalous readings in the presence of adversarial attacks that were introduced in the training and testing stages. The evaluation results revealed that the model achieved an average F1 score of 97% and an accuracy of 98%, despite the introduction of adversarial attacks. Internet of Healthcare Things (IoHT); anomaly detection; deep learning; convolutional long short-term memory (ConvLSTM); adversarial attacks +
Footnote †: journal: Medical  
to patients, and significant financial losses for healthcare entities using this healthcare system [4,5]. Healthcare systems now face a serious problem with anomalies, which can also result from faulty sensors and inaccurate observations from sensing devices. Anomaly detection is considered one of the best solutions for WBANs in terms of distinguishing abnormal data and may provide a reliable system to counter sensor faults and anomalous activities, along with an understanding of the factors affecting patients and healthcare organizations. Machine learning and statistical techniques have been applied in various studies to detect anomalies over the last few years. Different researchers have studied the employment of these techniques to detect anomalies in WBANs, and their findings support their effectiveness. However, these solutions still have some limitations in providing high accuracy within a limited timeframe without human intervention, in terms of the engineering of features. To overcome this challenge, deep learning is now being used to enhance the performance of anomaly detection models. Deep learning-based anomaly detection has great potential for increased security, due to its ability to detect anomalous behavior across data sources that cannot be identified using traditional security methods. However, the vulnerability of deep learning models to adversarial attacks is a fundamental obstacle to employing deep learning for security applications [4]. An adversarial attack occurs when adversarial attack data are fed as input into a deep learning model. One example of such an attack is a dataset in which some features have been purposely perturbed to confuse the deep learning model, in order to produce an incorrect prediction [6]. In this regard, the current work investigates the ability of a deep learning model employing a hybrid convolutional long short-term memory (ConvLSTM) approach to identify adversarial attacks in healthcare monitoring systems. The following is a summary of this paper's most significant contributions:  
1. Developing a model for detecting anomalies, utilizing the hybrid ConvLSTM technique, to detect adversarial attacks in WBANs. The technique is used to consider an attack in the training and testing phases and shows how adversarial attacks can potentially mislead the anomaly detection model. 2.