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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Ming Li
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Computer Science & IT

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Multi-Domain Authentication Protocol Based on Dual-Signature Zengyu Cai; Qikun Zhang; Ming Li; Yong Gan; Junsong Zhang
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 1: March 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i1.1164

Abstract

Today most multi-domain networks authentication systems provide data security and mutual authentication with asymmetric and traditional public key cryptography., there exist some problems, such as the overhead of passing certificates, the more complexity of management certificates and network bottlenecks and so on. These schemes can’t protect the safety of multi-domain interoperability in distributed network effectively. Aiming at these problems, the paper proposes an identity-based multi-domain authentication protocol among domains in large-scale distributed collaborative computing network. It adopts bilinear mapping and short signature technology to achieve mutual authentication between entities in different domains, which overcome the complexity of certificate transmission and bottlenecks in the scheme of PKI-based.  Analyzed shows that this scheme has anonymity, security and supporting mutual anonymous authentication and it is suitable to use in security alliance authentication mechanism in large distributed network.
Fuzzy Neural Networks Learning by Variable-Dimensional Quantum-behaved Particle Swarm Optimization Algorithm Jing Zhao; Ming Li; Zhihong Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 10: October 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

The evolutionary learning of fuzzy neural networks (FNN) consists of structure learning to determine the proper number of fuzzy rules and parameters learning to adjust the network parameters. Many optimization algorithms can be applied to evolve FNN. However the search space of most algorithms has fixed dimension, which can not suit to dynamic structure learning of FNN. We propose a novel technique, which is named the variable-dimensional quantum-behaved particle swarm optimization algorithm (VDQPSO), to address the problem. In the proposed algorithm, the optimum dimension, which is unknown at the beginning, is updated together with the position of swarm. The optimum dimension converged at the end of the optimization process corresponds to a unique FNN structure where the optimum parameters can be achieved. The results of the prediction of chaotic time series experiment show that the proposed technique is effective. It can evolve to optimum or near-optimum FNN structure and optimum parameters. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.2960
Co-Authors Jing Zhao Junsong Zhang Qikun Zhang Yong Gan Zengyu Cai Zhihong Wang