Internet of Things (IoT) health systems have severe issues distinguishing malicious from legitimate traffic and ensuring secure and efficient data transmission for real-time patient care. Existing solutions have high complexity, low dynamic attack adaptability, and low encryption strength. For the purpose of solving these problems, this study suggests a security-enhanced intelligent edge computing system that involves Normalized Distance-Based Encoding (NDBE) for effective feature extraction, Adaptive Layout Decomposition with Graph Embedding Neural Networks (ADGENN) for malicious data identification, Musical Chairs Optimization Algorithm (MCOA) for adaptive hyperparameter tuning, and a novel Light-weight Dynamic Elliptic Curve Cryptography with Schoof's Algorithm (LDECCSA) for data encryption protection. Together, these modules enhance classification efficiency, reduce computational costs, and facilitate low-latency, safe communication. Evaluated on the ToN-IoT and CICIoMT2024 dataset, the system achieves up to 99.87 % accuracy, 97 % throughput, and a low latency of 1.2 s, which performs better than current cutting-edge solutions by a large margin. The significance of this work is that it has the capacity to handle some of the most significant issues in healthcare. Systems are currently confronting, wherein IoT devices and edge computing have taken patient tracking to a new height, but also created gargantuan challenges such as cyberattacks, data breaches, and performance congestion. The major novelties are the application of NDBE for pre-processing network traffic, dynamic graph-based classification through ADGENN, resource-aware optimization through MCOA, and light-weighted, secure ECC with dynamic curve generation. While the model shows better efficiency and resilience, its dependence on pre-labeled datasets might restrict flexibility towards unknown real-world threats, and resource-limited IoT devices might struggle with heavy computation. In summary, the framework offers a real-world, scalable solution for real-time threat identification, secure data transfer, and effective healthcare surveillance in an IoT-based, cutting-edge healthcare environment.
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