A novel Hash algorithm construction based on chaotic neural network

Abstract

An 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-inthe-middle attacks, which can satisfy the performance requirements of Hash function.

Neural networks and chaos: Construction, evaluation of chaotic networks, and prediction of chaos with multilayer feedforward networks

“…Thus, this kind of networks can be trained to model a physical phenomenon known to be chaotic such as Chua's circuit [8]. Sometime a neural network, which is build by combining transfer functions and initial conditions that are both chaotic, is itself claimed to be chaotic [15].…”

Parallel Hash function construction based on chaotic maps with changeable parameters

“…The conventional Hash functions such as MD4 [1], MD5 [2], and SHA-1 are realized through the complicated methods based on logical XOR operation or multi-round iterations of some available cipher. Since there are some defects in conventional Hash function constructions, the recently proposed chaos-based Hash functions exhibit an attractive design direction [3][4][5][6][7][8][9][10][11][12][13][14][15]. Until recently, based on Baptista's method [3] in 1998 that the message text was encrypted as the number of iterations applied in the chaotic map in order to reach the region correspondent to that text, Wong developed a Hashing scheme [4] in 2003, which was built on the number of iterations of one-dimensional logistic map needed to reach the region corresponding to the character, along with a look-up table updated dynamically, and Xiao et al created a one-way Hash function-based construction based on the chaotic map with changeable parameter [5] in 2005, which employed 3-unit iterations of one-dimensional chaotic piecewise linear map to generate final Hash value extracted some bits from each iteration, and in the same year, Yi [6] proposed a Hash function based on chaotic tent maps, which operated on a message with arbitrary length to produce 2l-bit Hash value.…”

“…We employed a novel combined cryptographic and Hash algorithm based on chaotic control character [11] in 2009. In 2010, we proposed a novel Hash algorithm construction based on chaotic neural network [12] and also created Hash function based on the chaotic look-up table with changeable parameter [13]. However, all of the Hash algorithms mentioned above cannot be processed in a parallel mode, which can greatly improve the efficiency and speed of the Hash function.…”

Cuckoo Search Algorithm with Chaotic Maps

“…Chaos theory studies the behavior of dynamical systems that are highly sensitive to initial conditions, an effect which is popularly referred to as the butterfly effect. One of ways to make quantitative statements about the behavior of chaotic systems is chaotic map like Circle map [30], Gauss map [30], Logistic map [31], Piecewise map [32], Sine map [33], Singer map [34], Sinusoidal map [31], and Tent map [35], shown in Table 1. Additionally, the visualization of these chaotic maps with the initial point at 0.7 is plotted in Figure 1.…”