This study presents a comprehensive and secure architectural framework for the Internet of Medical Things (IoMT), integrating the foundational principles of the Confidentiality, Integrity, and Availability (CIA) triad along with authentication mechanisms. Leveraging advanced Machine Learning (ML) and Deep Learning (DL) techniques, the proposed system is designed to safeguard Patient-Generated Health Data (PGHD) across interconnected medical devices. Given the increasing complexity and scale of cyber threats in IoMT environments, the integration of Intrusion Detection and Prevention Systems (IDPS) with intelligent analytics is critical. Our methodology employs both standalone and hybrid ML & DL models to automate threat detection and enable real-time analysis, while ensuring rapid and accurate responses to a diverse array of attacks. Emphasis is placed on systematic model evaluation using detection metrics such as accuracy, False Alarm Rate (FAR), and False Discovery Rate (FDR), with performance validation through cross-validation and statistical significance testing. Experimental results based on the Edge-IIoTset dataset demonstrate the superior performance of ensemble-based ML models such as Extreme Gradient Boosting (XGB) and hybrid DL models such as Convolutional Neural Networks with Autoencoders (CNN+AE), which achieved detection accuracies of 96% and 98%, respectively, with notably low FARs. These findings underscore the effectiveness of combining traditional security principles with advanced AI-driven methodologies to ensure secure, resilient, and trustworthy healthcare systems within the IoMT ecosystem.

The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original integrity. Current approaches in Extractive Text Summarization (ETS) leverage the modeling of inter-sentence relationships, a task of paramount importance in producing coherent summaries. This study introduces an innovative model that integrates Graph Attention Networks (GATs) with Transformer-based Bidirectional Encoder Representations from Transformers (BERT) and Latent Dirichlet Allocation (LDA), further enhanced by Term Frequency-Inverse Document Frequency (TF-IDF) values, to improve sentence selection by capturing comprehensive topical information. Our approach constructs a graph with nodes representing sentences, words, and topics, thereby elevating the interconnectivity and enabling a more refined understanding of text structures. This model is stretched to Multi-Document Summarization (MDS) from Single-Document Summarization, offering significant improvements over existing models such as THGS-GMM and Topic-GraphSum, as demonstrated by empirical evaluations on benchmark news datasets like Cable News Network (CNN)/Daily Mail (DM) and Multi-News. The results consistently demonstrate superior performance, showcasing the model’s robustness in handling complex summarization tasks across single and multi-document contexts. This research not only advances the integration of BERT and LDA within a GATs but also emphasizes our model’s capacity to effectively manage global information and adapt to diverse summarization challenges.