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We present a fast and efficient hash algorithm based on a generalized chaotic mapping with variable parameters in this paper. We first define a generalized chaotic mapping by utilizing piecewise linear chaotic map and trigonometric functions. Then, we convert the arbitrary length of message into the corresponding ASCII values and perform 6-unit iterations with variable parameters and message values based on the generalized chaotic mapping. The final hash value is obtained by cascading extracted bits from iteration state values. We excessively evaluate the proposed algorithm in terms of distribution of hash value, sensitivity of hash value to the message and secret keys, statistical analysis of diffusion and confusion, analysis of birthday attacks and collision resistance, analysis of secret keys, analysis of speed, and comparison with other algorithms, and the results illustrate that the suggested algorithm is fast, efficient, and enough simple and has good confusion and diffusion capabilities, strong collision resistance, and a high level of security. [ABSTRACT FROM AUTHOR]

In this paper, we reconsider and analyze our previous paper a novel hash algorithm construction based on chaotic neural network, then present equal-length and unequal-length forgery attacks against its security in detail, and then propose a significantly improved approach by utilizing a method of complicated nonlinear computation to enhance the security of the original hash algorithm. Theoretical analysis and computer simulation indicate that the improved algorithm can completely resist the two kinds of forgery attacks and also shows other better performance than the original one, such as better message and key sensitivity, statistical properties, which can satisfy the performance requirements of a more secure hash function. [ABSTRACT FROM AUTHOR]

A new parallel chaotic Hash function, based on four-dimensional cellular neural network, is proposed in this paper. The message is expanded by iterating chaotic logistic map and then divided into blocks with a length of 512 bits each. All blocks are processed in a parallel mode, which is one of the significant characteristics of the proposed algorithm. Each 512-bit block is divided into four 128-bit sub-blocks, each of which is further separated into four 32-bit values and then the four values are mixed into four new values generated by chaotic cat map. The obtained four new values are performed by the bit-wise exclusive OR operation with four initial values or previously generated four values, and then, they are used as the inputs of cellular neural network. By iterating cellular neural network, another four values as the middle Hash value are generated. The generated values of all blocks are inputted into the compression function to produce the final 128-bit Hash value. Theoretical analysis and computer simulation indicate that the proposed algorithm satisfies the requirements of a secure Hash function. [ABSTRACT FROM AUTHOR]

n algorithm for constructing a one-way novel Hash function based on two-layer chaotic neural network structure is proposed. The piecewise linear chaotic map (PWLCM) is utilized as transfer function, and the 4-dimensional and one-way coupled map lattices (4D OWCML) is employed as key generator of the chaotic neural network. Theoretical analysis and computer simulation indicate that the proposed algorithm presents several interesting features, such as high message and key sensitivity, good statistical properties, collision resistance and secure against meet-in-the-middle attacks, which can satisfy the performance requirements of Hash function. [ABSTRACT FROM AUTHOR]