Tree parity machine-based One-Time Password authentication schemes

• Published in: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) Vol. 10; pp. 257 - 261
• Main Authors: Tieming Chen, Huang, S.H.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.06.2008
• Subjects: Artificial neural networks; Authentication; Cryptography; Protocols; Security; Servers; Synchronization
• ISBN: 1424418208; 9781424418206; 9781424432196; 1424432197
• ISSN: 2161-4393; 1522-4899; 2161-4407
• DOI: 10.1109/IJCNN.2008.4633799

One-time password (OTP) is always used as the strongest authentication scheme among all password-based solutions. Currently, consumer devices such as smart card have implemented OTP based two-factor authentications for secure access controls. Such solutions are economically sound without support of timestamp mechanisms. Therefore, synchronization of internal parameters in OTP models, such as moving factor or counter, between the client and server is the key challenge. Recently, a novel phenomenon shows that two interacting neural networks, called Tree Parity Machines (TPM), with common inputs can finally synchronize their weight vectors through finite steps of output-based mutual learning. The improved secure TPM can well be utilized to synchronize parameters for OTP schemes. In this paper, TPM mutual learning scheme is introduced, then two TPM-based novel OTP solutions are proposed. One is a full implementation model including initialization and rekeying, while the other is light-weight and efficient suitable for resource-constrained embedded environment. Security and performance on the proposed protocols are at final discussed.