As fundamental technical support to instantaneous E-healthcare services, Wireless Body Area Networks (WBANs) have attracted huge attention in the scientific and industrial community in recent years, which defines a unique wireless communication protocol for medical and healthcare circumstances. However, the concerns of the network and data security in WBANs have become an obstacle to the rapid development of it. In this paper, a double chaotic encryption method containing the natural randomness by adding physiological signal, electroencephalogram (EEG), is proposed. Initially, the three-dimensional eigenvalues contained deep multi-domain features are extracted as the initial factors of the chaos-based pseudo-random number generator to ensure the natural randomness of the initial values. Then, a hybrid pseudo-random number generator based on two chaotic maps, Piecewise linear Map (PLCM) and Coupled Logistic-Tent map (LTM), is proposed, which has the advantages of high randomness and low hardware complexity. Finally, the double chaotic encryption method based on the EEG eigenvalue for WBANs is presented. Various types of evaluations are performed to verify the efficiency and robustness of the proposed solution.

作为即时电子医疗服务的基础技术支持，无线体域网（WBAN）近年来引起了科学界和工业界的极大关注，它为医疗和保健环境定义了独特的无线通信协议。然而，WBANs对网络和数据安全的担忧已经成为其快速发展的障碍。在本文中，提出了一种通过添加生理信号脑电图（EEG）来包含自然随机性的双混沌加密方法。首先，提取包含深度多域特征的三维特征值作为混沌伪随机数发生器的初始因子，保证初始值的自然随机性。然后，提出了一种基于分段线性映射 (PLCM) 和耦合逻辑-帐篷映射 (LTM) 两种混沌映射的混合伪随机数生成器，具有随机性高、硬件复杂度低的优点。最后，提出了基于EEG特征值的WBAN双混沌加密方法。执行各种类型的评估以验证所提出解决方案的效率和稳健性。