
Wireless Body Area Networks (WBANs) have been extensively deployed to offer remote patient monitoring that facilitate timely diagnosis and medication. This has greatly helped reduce costs and stress on the limited healthcare resources. However, the exchange of sensory patient data across wireless public communication media exposes the communication process to a myriad of security threats. To curb these security challenges, past research work has deployed techniques such as identity-based and public key cryptosystems to develop schemes for this environment. Nevertheless, the complex mathematical computations in majority of these schemes render them inefficient for sensors. In this current work, we utilize an amalgamation of asymmetric cryptography and user biometrics to develop a robust authentication protocol for WBANs. The famous Burrows–Abadi–Needham (BAN) logic is then deployed to formally analyze the security posture of the developed scheme, with results indicating that it offers secrecy and reliability of the negotiated session keys. In addition, the informal security analysis shows that it is robust against typical WBAN attacks such as impersonation and privileged insiders. From the performance perspective, our protocol incurs the least communication and computation costs.
TK7885-7895, Authentication, Computer engineering. Computer hardware, Privacy, Electronic computers. Computer science, Security, QA75.5-76.95, Attacks, WBAN, Anonymity
TK7885-7895, Authentication, Computer engineering. Computer hardware, Privacy, Electronic computers. Computer science, Security, QA75.5-76.95, Attacks, WBAN, Anonymity
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 2 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
