Random Number Generator

Use this generatorto obtain an absolutely random secure, cryptographically safe number. It generates random numbers that can be used where unbiased results are critical, such as when shuffling a deck cards for a game of Poker or drawing numbers for the lottery, giveaway or sweepstake.

How do I pick what is a random number from two numbers?

This random number generator in order to choose an authentic random number from any two numbers. For example, to get an random number between 1 and 10 (including 10, simply enter the number 1 in the primary input and 10 in the next, after which press "Get Random Number". Our randomizer selects a number from 1 through 10, all at random. To generate a random number between 1 and 100, you can do the same but place 100 to the left of the picker. To simulate a dice roll the range must be 1-6 for a traditional six-sided dice.

If you want to generate many unique numbers, you need to select the number of numbers you require from the drop-down menu below. As an example, selecting to draw 6 numbers out of the possible numbers one to 49 could be like simulating a lottery draw for an event using these parameters.

Where are random numbersuseful?

You could be planning an appeal for charity, or a sweepstakes or a sweepstakes. If you are required to draw winners, this generator is the perfect tool for you! It's totally independent and out completely of the realm of influence and therefore you can guarantee your fans of the fairness of the draw, which might not be so if you are using traditional methods such as rolling dice. If you're looking to choose different participants choosing the appropriate number of unique numbers that you would like to be generated from our random number picker and you're all set. But, it's usually best to draw the winners one at a time, to keep the excitement longer (discarding repetition draws in the process).

A random number generator is also handy if you want to decide which player will start first in a particular game or activity, such as board games, games of sport and sporting competitions. This is also true when you have to determine the participation order for a number of players or participants. Making a selection at random or randomizing the names of participants is dependent on the quality of randomness.

These days, many lotteries, both private and government-run, and lottery games are using software RNGs rather than traditional drawing techniques. RNGs are also used to determine the outcome of all current slot machines.

In addition, random numbers are also valuable in statistical and simulations when they are created from different distributions than the standard, e.g. A normal distribution, binomial distribution such as a power distribution, pareto distribution... In these use-cases a more sophisticated software is needed.

Generating a random number

There's a philosophical dilemma concerning the definition of "random" is, but its main characteristic is surely uncertainness. We are not able to talk about uncertainty of one number, since that number is exactly what it is. However, we can talk about the unpredictability of a series that includes numbers (number sequence). If the sequence of numbers is random the chances are that you'll not be in a position to predict the next number in the sequence even though you are knowing any part of the sequence up to now. There are examples in the rolling of a fair dice as well as spinning a well-balanced wheel and drawing lottery balls from a sphere, and the classic flip of a coin. Whatever number of coins flips, dice rolls roulette spins, lottery draws you watch there is no way to improve your chances of knowing the next number in the sequence. If you are interested in physics the best instance of random movement will be Browning motion of fluid particles, gas, or other gas.

Based on the above information and the fact that computers are completely dependent, which means that their output is entirely dependent on the input they provide the computer. One could say that we can't generate the concept of a random number on a computer. But, this can only be partially true since the results of a dice roll or a coin flip can also be determinate, provided you know the current state of the system.

The randomness of our number generator originates from physical processes - our server collects noise from device drivers and other sources to create an entropy pool from which random numbers are created ^[11.

Randomness is caused by random sources.

As per Alzhrani & Aljaedi ^{[2according to Alzhrani & aljaedi [2} they identify four random sources that are used in the seeding of a generator made up of random numbers, two of which are used in our number generator:

• The disk will release entropy whenever the driver calls it seeking time of block layer request events.
• Interrupting events caused by USB and other driver software for devices
• System values such as MAC addresses, serial numbers and Real Time Clock - used for initializing the input pool, usually for embedded systems.
• Entropy generated by input hardware keyboard and mouse actions (not utilized)

This puts the RNG used in this random number software in compliance with the requirements from RFC 4086 on randomness required for security ^[3].

True random versus pseudo random number generators

It is a pseudo-random generator (PRNG) is an unreliable state machine that has an initial value referred to as the seed ^[4]. Each time a request is made, a transaction function computes the next state internally and output functions generate the actual number in accordance with the state. A PRNG generates the periodic sequence of values that depends only on the initial seed provided. An example would be an linear congruent generator such as PM88. Thus, knowing even the short sequence of generated values it is possible to figure out the seed that was used and, therefore, determine the value that will be generated next.

In other words, a Cryptographic pseudo-random generator (CPRNG) is one of the PRNGs in that it can be identified if the internal state of the generator is known. However, assuming the generator was seeded with enough energy and that the algorithms have the necessary properties, these generators aren't able to expose large amounts of their internal data, thus you'd need an immense quantity of output before you could make a strong attack on them.

A hardware RNG is built on the unpredictable physical phenomena, known as "entropy source". Radioactive decay is more precise. The times at which the radioactive source degrades, can be described as a phenomenon that is similar to randomness as it gets as decaying particles are easy to identify. Another example is heat variations - some Intel CPUs feature a detection for thermal noise inside the silicon of the chip that outputs random numbers. Hardware RNGs are however typically biased and, more important, are restricted in their capacity to create sufficient entropy in practical spans of time, due to the low variance of the natural phenomenon they sample. Therefore, a different type of RNG is needed for practical applications that is the real random number generator (TRNG). In this, cascades that are made up of hardware-based RNG (entropy harvester) are used to periodically renew a PRNG. If the entropy is high enough the PRNG behaves as it is a TRNG.