#RANDOM NUMBER GENERATOR GENERATOR#
#RANDOM NUMBER GENERATOR FULL#

Compared with the former two methods, the characteristics of chaos, such as nonperiodicity, wide spectrum, unpredictability, and sensitivity to initial conditions, are in good agreement with the properties of random numbers.

#RANDOM NUMBER GENERATOR FULL#

But the disadvantage is that it is not suitable for full custom integrated circuit (IC), and the randomness of circuit implementation is low.

#RANDOM NUMBER GENERATOR GENERATOR#

The quality of the random sequence generated by the true random number generator is largely determined by the root mean square (RMS) value of the phase jitter of the low frequency oscillator.

For the oscillator-based TRNGs, the random source is the phase jitter noise in the ring oscillator in Complementary Metal Oxide Semiconductor (CMOS) circuit.

In practice, due to the influence of some nonideal factors, such as the limited bandwidth of the amplifier, misalignment, periodic noise of the power supply coupled to the system, the randomness of the random number sequence generated by the system will be affected. For the TRNGs based on thermal noise, the resistance noise is amplified to a suitable range by an ideal amplifier, and then processed by a comparator to compare the amplified noise voltage with the reference level to obtain a digital random signal. In true random number generators (TRNGs), there are three main types of entropy sources: thermal noise on resistors and capacitors, phase jitter of oscillating signals, chaos and others, as shown in Figure 1. They could be used in systems with high security requirements. Compared with PRNs, TRNs not only have good statistical characteristics but also have good unpredictability. Unlike PRNs, TRNs cannot be generated by pure mathematical random algorithms, but only by random physical processes. PRNs refer to the extension of one seed into another long output sequence by a determined algorithm, which are generally repeatable, so they are widely used in the field of simulation and testing. ДЄccording to the different random sequence generated, random numbers can be divided into two categories, namely pseudo-random numbers (PRNs) and true random numbers (TRNs), as shown in Figure 1. In most cryptographic algorithms, random number is an indispensable element, and random number generator (RNG) has important applications in the field of information security, such as generating parameters of public key cryptosystems (such as ECC, RSA) or image encryption. In the field of information security, encryption algorithm, and key generation are important factors of encryption system they must be unpredictable. In recent years, with the rapid development of the Internet, the requirements for information security in various fields are getting higher and higher, and the security issues are getting more and more attention. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of TRNGs based on chaos. Finally, quantitative results are given for the described methods in which they were evaluated, following up with a discussion of the results. We also devote a part of the paper to review TRNGs based on current-mode chaos for this problem. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. The main approaches and challenges are exposed to help researchers decide which are the ones that best suit their needs and goals. Firstly, the two kinds of popular chaotic systems for generating TRNs based on chaos, including continuous time chaotic system and discrete time chaotic system are introduced. This survey paper intends to provide a systematic review of true random number generators (TRNGs) based on chaos. These demands coincide with the rise of TRNs generating approaches in chaos field. Chaos has good features of sensitive dependence on initial conditions, randomness, periodicity, and reproduction. True random numbers (TRNs) have better randomness and unpredictability in encryption and key than pseudorandom numbers (PRNs). Random numbers are used in many cryptographic protocols, key management, identity authentication, image encryption, and so on. With the rapid development of communication technology and the popularization of network, information security has been highly valued by all walks of life.

Random number generator