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Channel characteristics aware zero knowledge proof based authentication scheme in body area networks

Ad Hoc Networks, ISSN: 1570-8705, Vol: 112, Page: 102374
2021
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Metric Options:   Counts1 Year3 Year

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Article Description

Wireless body area network (WBAN) is a network of wearable devices placed on the body of patients to collect and transmit their biomedical data to medical servers through open wireless channels. These collected data are sensitive and their transmission via the open wireless channels makes them vulnerable to attacks by unauthorized users. Therefore, secure authentication and data encryption mechanisms in WBAN are essential. In the past few years, several zero knowledge proof (ZKP) and commitment technique based schemes for WBAN were proposed to provide lightweight authentication and data encryption for intra-WBAN communication. However, these schemes are susceptible to node compromise and impersonation attacks and cannot provide security for inter-WBAN communication. Motivated by these limitations, we first propose a compromise and impersonation attacks resistant (CIAR) authentication scheme based on ZKP, commitment technique, and received signal strength (RSS), which could identify attackers that have compromised nodes and attempt to impersonate them. To ensure the security of the inter-WBAN communication, we then propose a channel characteristic aware (CCA) authentication scheme based on the ZKP and commitment technique. We performed security and performance analyses to validate the resilience of the schemes to various attacks and their effectiveness in terms of resources. Moreover, we conducted extensive experiments in indoor and outdoor areas to demonstrate the security strength of our schemes. The experimental results as well as the performance and security analyses show that our CIAR-ZKP scheme overcomes the security weaknesses in previous schemes at an equal cost. Moreover, the results of the CCA-ZKP scheme indicate that it can effectively identify 92% of attack attempts while triggering false alarms on merely 11% of legitimate traffic.

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