Structure-Varying Complex Network Chaotic Model and Its Hardware Implementation

Existing research works on chaotic systems focus on enhancing the complexity of chaotic systems while neglecting their practicality. Especially in scenarios with constrained computing precision, most chaotic systems are impractical for applications due to the dynamical degradation. In order to promote the potential for applications of chaotic systems, this paper presents a novel structure-varying complex network chaotic (SVCNC) model. The SVCNC model leverages network structure variability to maintain reliability in low-precision environments and robustness against dynamic changes and potential disruptions. Experimental results demonstrate excellent properties of the SVCNC model. N-dimensional SVCNC model has
$n$
positive Lyapunov exponents that indicate high nonlinearity and complexity. Chaotic sequences generated by the model have long periods and strong randomness. To validate its application potential, an 8-bit fixed point pseudo-random number generator (PRNG) is designed based on the SVCNC model. The PRNG is further implemented on the field programmable gate array (FPGA) hardware platform, which achieves decent performance. Completed security analyses are conducted from a cryptographic point of view. Results verified that the proposed PRNG features large key space, extreme key sensitivity, good statistical properties and randomness, which can resist brute force attacks, statistical attacks and NIST SP800-22 and TestU01 test analysis. These findings establish the SVCNC model as a robust framework for complex networks and highlight its potential in high-security applications with resource-constrained environments.