###### Abstract  
The increasing use of IoHT devices in healthcare has brought about revolutionary advancements, but it has also exposed some critical vulnerabilities, particularly in cybersecurity. IoHT is characterized by interconnected medical devices sharing sensitive patient data, which amplifies the risk of cyber threats. Therefore, ensuring healthcare data's integrity, confidentiality, and availability is essential. This study proposes a hybrid deep learning-based intrusion detection system that uses an Artificial Neural Network (ANN) with Bidirectional Long Short-Term Memory (BLSTM) and Gated Recurrent Unit (GRU) architectures to address critical cybersecurity threats in IoHT. The model was tailored to meet the complex security demands of IoHT and was rigorously tested using the Electronic Control Unit ECU-IoHT dataset. The results are impressive, with the system achieving 100% accuracy, precision, recall, and F1-Score in binary classifications and maintaining exceptional performance in multiclass scenarios. These findings demonstrate the potential of advanced AI methodologies in safeguarding IoHT environments, providing high-fidelity detection while minimizing false positives. IoHT; deep learning; intrusion detection systems; healthcare; cybersecurity +
Footnote †: journal: Medical Security  
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Footnote †: journal: Medical Security  
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Footnote 0: _Applied_  
Study [9] proposed a novel intrusion detection system designed specifically for IoT networks. The system successfully identified various types of assaults by using a hybrid approach that included the Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models. The suggested approach demonstrated its suitability for a diverse array of IoT applications. The UNSW NB15 dataset was used, with a validation ratio of 70% for training and 30% for test validation. The proposed model was experimentally shown to achieve an accuracy of 98% across various IoT scenarios. Undetected attacks on the IoT may lead to significant service disruptions, causing substantial financial losses. Furthermore, it presents the potential risk of compromising one's identity. The real-time detection of intrusions on IoT devices is crucial for ensuring the reliability, security, and profitability of IoT-enabled services. A study proposed a novel intrusion detection system for IoT devices using DL techniques [10]. The system utilized a four-layer deep Fully Connected (FC) network architecture to identify malicious traffic that could launch assaults on interconnected IoT devices. The system under consideration was designed to be independent of communication protocols to mitigate the challenges associated with deployment. The system under consideration exhibited consistent and dependable performance when subjected to simulated and actual intrusions, as shown by the experimental performance study. The system had a mean accuracy of 93.74% in identifying and detecting several types of cyber assaults, including Blackhole, Distributed Denial of Service (DDOS), Opportunistic Service, Sinkhole, and Workhole attacks. On average, the suggested intrusion detection system's precision, recall, and F1-Score were 93.71%, 93.82%, and 93.47%, respectively. That study's deep learning-based intrusion detection system (IDS) demonstrated a commendable average detection rate of 93.21%. This performance level is deemed suitable for enhancing the security of IoT networks. Conventional IDSs for advanced network-based attack detection encounter difficulties in network environments that employ typical IoT protocols and operate on a centralized network architecture, such as a software-defined network (SDN). In [11], the authors proposed a methodology that utilizes LSTM to identify network assaults inside IoT networks, with IDS enabled by SDN. The authors provided a comprehensive assessment of the performance of ML and DL models using two datasets designed explicitly for Software-Defined Networking in the Internet of Things (SDNIoT) applications.