Improved Anonymous Hybrid Authentication Scheme for Body Area Network Utilizing Channel Characteristics

Although wireless body network (WBAN) plays a vital role in revolutionizing healthcare delivery, it has also brought security concerns due to the sensitive nature of the patients’ biomedical data being measured by the sensors in the network. Thus, authentication is essential for the safety of the patients in WBAN. Recently, Koya and Deepthi proposed an anonymous hybrid authentication scheme using a physiological signal to fix the security limitations of the existing anonymous authentication approaches in WBAN. However, their scheme lacks forward secrecy and requires that additional sensing hardware be attached to each sensor in WBAN, which is not only cost-prohibitive, but can lead to compatibility issues with legacy systems. To overcome these limitations, we propose an improved anonymous hybrid authentication scheme using physical layer (PHY) channel characteristics. The key idea in the proposed scheme is to replace the physiological signal used in the Koya and Deepthi scheme with a received signal strength (RSS) information already available in the wearable devices in WBAN as an identity identifier. We conduct security analysis to show the security strength of our approach and performance analysis to prove its computational efficiency. Moreover, we conduct experiments on human volunteers in indoor and outdoor locations to show the robustness of the proposed approach. The results demonstrate that our scheme can provide anonymous authentication and identify 90% of attack attempts in WBAN with less computation cost and without any additional sensing hardware.