This paper explores novel strategies to strengthen the security of Hybrid Wireless Body Area Networks (HyWBANs), essential in smart healthcare and Internet of Things (IoT) applications. Recognizing the vulnerability of HyWBAN to sophisticated cyber-attacks, we propose an innovative combination of semantic communications and jamming receivers. This dual-layered security mechanism protects against unauthorized access and data breaches, particularly in scenarios involving in-body to on-body communication channels. We conduct comprehensive laboratory measurements to understand hybrid (radio and optical) communication propagation through biological tissues and utilize these insights to refine a dataset for training a Deep Learning (DL) model. These models, in turn, generate semantic concepts linked to cryptographic keys for enhanced data confidentiality and integrity using a jamming receiver. The proposed model demonstrates a significant reduction in energy consumption compared to traditional cryptographic methods, like Elliptic Curve Diffie-Hellman (ECDH), especially when supplemented with jamming. Our approach addresses the primary security concerns and sets the baseline for future secure biomedical communication systems advancements.

翻译：本文探索了加强混合无线体域网（HyWBAN）安全的新型策略，该网络在智慧医疗与物联网（IoT）应用中至关重要。针对HyWBAN易受高级网络攻击的脆弱性，我们提出了一种语义通信与干扰接收机的创新组合方案。这种双层安全机制可防护未授权访问和数据泄露，尤其适用于体内-体表通信信道场景。通过开展全面的实验室测量，我们研究了生物组织中的混合（射频与光学）通信传播特性，并利用这些认知优化了用于训练深度学习（DL）模型的数据集。这些模型进而生成与加密密钥关联的语义概念，配合干扰接收机实现增强的数据机密性与完整性。与传统加密方法（如椭圆曲线迪菲-赫尔曼密钥交换协议，ECDH）相比，所提模型在能耗方面显著降低，尤其当辅以干扰机制时。本研究方法不仅解决了核心安全关切，更为未来安全生物医学通信系统的发展奠定了基准。