Integrating Large Language Models (LLMs) with Knowledge Graphs (KGs) enhances the interpretability and performance of AI systems. This research comprehensively analyzes this integration, classifying approaches into three fundamental paradigms: KG-augmented LLMs, LLM-augmented KGs, and synergized frameworks. The evaluation examines each paradigm’s methodology, strengths, drawbacks, and practical applications in real-life scenarios. The findings highlight the substantial impact of these integrations in fundamentally improving real-time data analysis, efficient decision-making, and promoting innovation across various domains. In this paper, we also describe essential evaluation metrics and benchmarks for assessing the performance of these integrations, addressing challenges like scalability and computational overhead, and providing potential solutions. This comprehensive analysis underscores the profound impact of these integrations on improving real-time data analysis, enhancing decision-making efficiency, and fostering innovation across various domains.

The Internet of Things (IoT) is seeing significant growth, as the quantity of interconnected devices in communication networks is on the rise. The increased connectivity of devices has heightened their susceptibility to hackers, underscoring the need to safeguard IoT devices. This research investigates cybersecurity in the context of the Internet of Medical Things (IoMT), which encompasses the cybersecurity mechanisms used for various healthcare devices connected to the system. This study seeks to provide a concise overview of several artificial intelligence (AI)-based methodologies and techniques, as well as examining the associated solution approaches used in cybersecurity for healthcare systems. The analyzed methodologies are further categorized into four groups: machine learning (ML) techniques, deep learning (DL) techniques, a combination of ML and DL techniques, Transformer-based techniques, and other state-of-the-art techniques, including graph-based methods and blockchain methods. In addition, this article presents a detailed description of the benchmark datasets that are recommended for use in intrusion detection systems (IDS) for both IoT and IoMT networks. Moreover, a detailed description of the primary evaluation metrics used in the analysis of the discussed models is provided. Ultimately, this study thoroughly examines and analyzes the features and practicality of several cybersecurity models, while also emphasizing recent research directions.

The proliferation of digital infrastructures and the Internet of Things (IoT) has led to a rapid increase in interconnected devices, exposing modern systems to increasingly sophisticated cyber threats. Intrusion detection in such environments remains a major challenge due to limited device resources, evolving attack vectors, and diverse traffic patterns. Traditional systems often fall short in scalability and adaptability when facing these modern threats. This paper introduces MM-NIDS, a novel multimodal fusion framework for NetFlow-based intrusion detection. The framework combines four complementary NetFlow-derived data representations (numerical, textual, graph-based, and quantum-inspired), each modeled using transformer-based architectures, including FT-Transformer and ELECTRA-Small. Feature embeddings are constructed using robust engineering techniques, while predictions from the four base models are integrated through five post hoc fusion strategies: averaging-based fusion, weighted averaging, confidence-based fusion, and two meta-fusion methods based on a Multi-Layer Perceptron (MLP) and Extreme Gradient Boosting (XGBoost). Extensive cross-dataset evaluations on four public NetFlow-based benchmarks confirm the system’s robustness, with the text-based model (M2) consistently achieving the highest individual performance. Fusion approaches provided modest and dataset-dependent improvements in detection balance, especially for underrepresented attacks. A detectability hypothesis was proposed and validated, showing that NetFlow features are particularly effective for volumetric and scan-based attacks but less so for stealthy, payload-driven threats. These findings highlight the potential of MM-NIDS for deployment in critical infrastructure, industrial IoT, and smart environments, suggesting that future work should incorporate deeper semantic or payload-level features to enhance the detection of evasive threats further.