A Remote Password Authentication Scheme for Multiserver Architecture Using Neural Networks
Li-Hua Li, Iuon-Chang Lin, and Min-Shiang Hwang

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. In this paper, 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.

Index Terms—Neural network, password authentication, remote login, security.

I. INTRODUCTION

RECENTLY, computer security has become an important issue. More and more systems have added control to the access process for avoiding illegitimate users reading sensitive information. Password authentication is one of the mechanisms that is widely used to authenticate a legitimate user. Conventional password authentication schemes are suited to solve the privacy and security problem for single servers under a client/server architecture. However, the use of computer
networks and information technology has grown spectacularly. Many network architectures have become multiserver environments. In conventional password authentication schemes, a network user not only needs to log into various remote
servers with repetitive registration, but also needs to remember the various user identities and passwords. Another problem in using the traditional password authentication method is that a server must maintain a password table that stores each user’s ID and password. Therefore, the server requires extra memory space to store the password table. The table is shown in Fig. 1(a) [10]. When a user logs into a computer, he/she types in the ID and password. The server searches the password table and checks if the password is legal. However, this method is dangerous. The password information table could be read or altered by an intruder. An intruder can also append a new ID and password into the table.


※ 这篇文章创新度很高。
一位教授是专长在 Neural Network ，另外一位教授则是在 Information Security，所以他们合作做这篇论文发表在 IEEE TRANSACTIONS ON NEURAL NETWORKS 是相当了不起的事。
之前，我贴上 Fuzzy + Information Security 文章，现在补上 Neural Network + Information Security 应该更接近 AI + Information Security 的轮廓。

[招生]科锐逆向工程师培训(2024年11月15日实地，远程教学同时开班, 第51期)

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