An Enhanced Authenticated Key Agreement Protocol with a Neural Network-based Model for Joining-Phase in Mobile Environments

Abstract

Providing secure communication for mobile environment is very important. In such environments, it is envisaged that users (through one of their mobile devices and via some network access technologies) should be able to seamlessly discover, select, and access a rich offering of services from a range of service providers. Password-based and ID-based authentication schemes are widely used but they still have potential threats that the passwords and verification table may be read, or altered by intruder. Moreover, the user’s privacy is not achieved especially if a principal’s privacy is to be protected. On the other side, Neural Network has been attracting more and more researchers since the past decades. The properties such as parameter sensitivity, random similarity, learning ability, etc., make it very suitable for information protection and privacy preserving. In this paper we present a Neural based secure joining phase with a biometric based Authenticated Key Agreement (NB-EC-SAKA). Our proposed protocol provides privacy, biometric-based secure mutual authentication, key establishment and key confirmation over an untrusted network. Our protocol is a based on EC-SAKA protocol [16], on Elliptic Curve Cryptography, and on EC-digital signature. The new proposal relies on [16] to achieve many of the required security and performance properties including dictionary attacks, ManIn-The middle attack, perfect forward secrecy which protects past sessions and passwords against future compromise, etc. This paper presents a review on the biometric authentication and neural networks techniques and presents a new scheme where user’s security and privacy are preserved. The purpose of our scheme is to ensure that the rendered services are accessed only by a legitimate user and not anyone else and while privacy is preserved.