The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A remote password authentication scheme for multiserver architecture using neural networks
Abstract
Conventional remote password authentication schemes allow a serviceable server to authenticate the legitimacy of a remote login user. However, these schemes are not used for multiserver architecture environments. We present a remote password authentication scheme for multiserver environments. The password authentication system is a pattern classification system based on an artificial neural network. In this scheme, the users only remember user identity and password numbers to log in to various servers. Users can freely choose their password. Furthermore, the system is not required to maintain a verification table and can withstand the replay attack.
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Citations
Cites
A public key cryptosystem and a signature scheme based on discrete logarithms
Elgamal, T. (1985) · IEEE Transactions on Information Theory · doi:10.1109/tit.1985.1057074 · Cited by 8,059
Data encryption standard (DES)
Biryukov, A., & Cannière, C. (2006) · Encyclopedia of Cryptography and Security · doi:10.1007/0-387-23483-7_94 · Cited by 57
Cited by
A secure dynamic ID based remote user authentication scheme for multi-server environment
Liao, Y.-P., & Wang, S.-S. (2009) · Computer Standards & Interfaces · doi:10.1016/j.csi.2007.10.007 · Cited by 379
Hsiang, H.-C., & Shih, W.-K. (2009) · Computer Standards & Interfaces · doi:10.1016/j.csi.2008.11.002 · Cited by 301
Metadata
- Authors
- Li, L.-H., Lin, L.-C., & Hwang, M.-S.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2001
- Volume
- 12
- Issue
- 6
- Pages
- 1498-1504
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2155392226
- Cited by
- 328
- References
- 2
- FWCI
- 14.46
- Type
- journal article
- Language
- en
- Open access
- closed
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- Li, L.-H., Lin, L.-C., & Hwang, M.-S. (2001). A remote password authentication scheme for multiserver architecture using neural networks. IEEE Transactions on Neural Networks. https://doi.org/10.1109/72.963786
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