###### Abstract
In this environment, smart medical devices such as glucometers and blood pressure monitors are interconnected, enabling seamless communication and the sharing of vital medical data [23]. Healthcare practitioners and facilities then use this information to provide top-quality care and support. However, it is essential to remember that IoHT devices collect sensitive health data, which makes security and privacy protection critical [24]. With intelligent monitoring and data transmission to an IoHT server, these devices transform how we care for patients. Protecting IoT devices from cyber threats is paramount, as hardware and software can be vulnerable to attacks [25]. In [26], a deep neural network-based cyber-attack detection system is developed using artificial intelligence on the ECU-IoHT dataset to detect cyber-attacks in the Internet of Health Things ecosystem. The proposed deep neural network system obtained an improved performance accuracy of 99.85 percent, a mean area under the receiver operator characteristic curve of 0.99, and a false positive rate of 0.01. Paper [27] suggested a deep neural network in federated learning (DNN-FL) to detect security-threatening anomalies in IoHT data. The authors evaluated their proposal's detection effectiveness using metrics such as accuracy and precision. Using the wustl-ehms-2020 and ECU-IoHT datasets, the proposed DNN-FL was validated. It detected attacks with 91.40% accuracy in the wustl-ehms-2020 dataset and 98.47% in the binary classification on the ECU-IoHT dataset. In [28], the authors proposed a framework for developing IoT context-aware security solutions to detect malicious traffic in IoT healthcare environments. The proposed framework consisted of an IoT traffic generator utility that generated standard and malicious traffic using an IoT-based Intensive Care Unit (ICU) use case. Six commonly used ML classifiers were trained and evaluated on the generated dataset for malicious and traditional traffic detection in the IoT healthcare environment. They examined the efficacy of every trained ML classifier. The random forest classifier performed the best among the six ML classifiers, with 99.7068% precision, 99.79% recall, 99.51% accuracy, and 99.65% F1-Score. The main contribution of this work involves building an intrusion detection model in the IoHT model, which covers a range of cyber-attack scenarios while maintaining the confidentiality of medical information. We explore different intrusion detection techniques, including deep learning. Deep learning is a powerful option for intrusion detection in IoHT because of its ability to self-learn, adapt, and generalize. Our work is based on leveraging the ECU-IoHT dataset [29] for evaluation. This dataset enables us to assess our model's effectiveness in detecting a wide range of cyber-attack scenarios while ensuring the confidentiality of sensitive medical information. A comparative analysis systematically evaluates our approach against existing methods to highlight its advantages and unique contributions. This comparison can be performed using various standard metrics, which provide quantitative proof of our model's superior performance. Explicitly remarking on the limits of current approaches, such as their inability to detect new and unknown adversarial attacks effectively, sets the stage for showcasing the innovative aspects of our solution. Our proposed model, which integrates a hybrid deep learning model, addresses these limitations, and we can clearly articulate the improvements. Emphasizing our model's robust performance on a realistic ECU-IoHT dataset and its adaptability to various IoHT scenarios highlights its practical applicability and relevance.