Enhancing One-Dimensional Chaotic Map Based on Bitstream Dividing Model

Abstract

The chaotic map has attracted more researchers in chaotic cryptography domain. That is because to large similarity among their characteristics such as unpredictability and sensitivity to an initial condition. Moreover, chaotic maps are a source of entropy that provide diffusion and confusion strength in cryptographic applications. Although existing one dimensional chaotic maps are widely used, many security issues are found in their behaviors. This paper tackles those issues through the new proposed solution model that uses bitstream dividing model. The proposed model uses the fixed-point format to extract bitstream and dividing a chaotic state into N equal parts, each part is a new chaotic state. In order to show the effectiveness of the proposed model, the unimodal maps are used in the simulation, it is shown that the proposed model has better chaotic complexity and performance than the corresponding maps. Moreover, it is a simple structure but effective and can be used as a chaotification model to generate digital chaos. In chaos-based cryptography, the enhanced maps have a better security strength which is highly needed for the development of chaos-based ciphers.