Biblio

In the last few decades, the relative simplicity of the logistic map made it a widely accepted point in the consideration of chaos, which is having the good properties of unpredictability, sensitiveness in the key values and ergodicity. Further, the system parameters fit the requirements of a cipher widely used in the field of cryptography, asymmetric and symmetric key chaos based cryptography, and for pseudorandom sequence generation. Also, the hardware-based embedded system is configured on FPGA devices for high performance. In this paper, a novel stream cipher using chaotic logistic map is proposed. The two chaotic logistic maps are coded using Verilog HDL and implemented on commercially available FPGA hardware using Xilinx device: XC3S250E for the part: FT256 and operated at frequency of 62.20 MHz to generate the non-recursive key which is used in key scheduling of pseudorandom number generation (PRNG) to produce the key stream. The realization of proposed cryptosystem in this FPGA device accomplishes the improved efficiency equal to 0.1186 Mbps/slice. Further, the generated binary sequence from the experiment is analyzed for X-power, thermal analysis, and randomness tests are performed using NIST statistical.

Blum-Blum-Shub (BBS) is a less complex pseudorandom number generator (PRNG) that requires very large modulus and a squaring operation for the generation of each bit, which makes it computationally heavy and slow. On the other hand, the concept of elliptic curve (EC) point operations has been extended to PRNGs that prove to have good randomness properties and reduced latency, but exhibit dependence on the secrecy of point P. Given these pros and cons, this paper proposes a new BBS-ECPRNG approach such that the modulus is the product of two elliptic curve points, both primes of length, and the number of bits extracted per iteration is by binary fraction. We evaluate the algorithm performance by generating 1000 distinct sequences of 106bits each. The results were analyzed based on the overall performance of the sequences using the NIST standard statistical test suite. The average performance of the sequences was observed to be above the minimum confidence level of 99.7 percent and successfully passed all the statistical properties of randomness tests.