###### Abstract
Article [15] presented the implementation of an intelligent intrusion detection system designed to identify and detect assaults against IoT devices. A deep learning system was used to identify fraudulent network traffic inside the Internet of Things. The identification solution guaranteed operational security and facilitated interoperability across connection protocols in the Internet of Things. IDS is a widely used network security technology for network protection. Based on the findings obtained from their experimental analysis, the suggested architecture for intrusion detection exhibited a high level of proficiency in accurately identifying genuine global intrusions. Using a neural network to detect assaults demonstrated a high level of effectiveness. Furthermore, there is a growing emphasis on providing cybersecurity solutions that prioritize the needs and preferences of users. This requires collecting, processing, and analyzing substantial data traffic volumes and connections inside 5G networks. After rigorous testing, the autoencoder model exhibited superior performance by significantly reducing detection time and enhancing detection accuracy. A remarkable accuracy rate of 99.76% was attained using the suggested methodology. As the volume of sensitive data transmitted in IT infrastructures increases, healthcare individuals and businesses that generate supplementary data for users have become attractive targets for cybercriminals. IoHT devices must be protected to preserve electronic healthcare data. Researchers have attempted to develop a robust IDS to secure healthcare environments. The authors of [16] presented a hybrid deep learning methodology for IoT botnet malware detection that incorporates CNN-BLSTM-GRU to facilitate efficient multiclass malware family detection. Accuracy, detection rate, and receiver operating characteristic area under the curve (ROC AUC) were performance metrics used to evaluate the hybrid deep learning model proposed by the authors. IoT-based botnet attack detection attained 98.34% accuracy and the suggested hybrid CNN-BLSTM-GRU deep learning-based botnet attack detection system obtained 99.25% accuracy. Study [17] proposed a new cybersecurity method using deep learning to facilitate the detection of intrusions in the social Internet of Things. The performance of the deep model was compared to that of the conventional machine learning approach, and the performance of the distributed attack detection system was compared to that of the centralized detection system. Using the NSL-KDD dataset, the experiments demonstrated that the overall detection accuracy increased from 96% to over 99%. The healthcare industry increasingly applies IoT and artificial intelligence (AI) technologies to enhance services. IoT-enabled hospital devices improve patient safety, reduce costs, and increase healthcare accessibility [18]. AI and IoT play vital roles in medical diagnostics, real-time patient monitoring, medical image analysis, treatment planning, drug discovery, and personalized healthcare [19]. Smart healthcare systems leverage wearable devices, IoT, and AI to access medical information, enhancing efficiency and personalization in healthcare services [20]. ML in IoT allows for pattern recognition and predictive capabilities, benefiting healthcare through automated patient monitoring and data management [21]. The combination of AI and IoT in healthcare is revolutionizing decision-making and resource management but also presents challenges, including cybersecurity, energy consumption, and privacy concerns. Researchers are looking to enhance cyber-attack detection in IoT by utilizing artificial intelligence, machine learning, and deep learning methods to identify new and evolving threats while minimizing false positive detections. IoHT is a network combining various hardware platforms, software, and medical devices to support healthcare information technology [22].