Random Number Generator

Utilize this generatorto obtain an absolutely random and cryptographically safe number. It generates random numbers that can be used where unbiased results are critical, such as playing shuffled decks of cards in a game of poker or drawing numbers for giveaways, lottery or sweepstake.

How to pick what is a random number from two numbers?

You can utilize this random number generator to generate an authentic random number among any two numbers. For instance, to generate an random number in the range of one to 10 (including 10, input 1 to the top box and 10 in the secondfield, after which press "Get Random Number". Our randomizer will pick a number from 1 through 10, all at random. For generating the random number between 1 and 100, repeat the process as above, except that you put 100 for the other field within the randomizer. To simulate a dice roll the range should be from 1 to 6 for a standard six-sided dice.

If you want to generate an additional unique number select the number of numbers you require by using the drop-down box below. In this case, choosing to draw 6 numbers out of the possible numbers 1 to 49 options would be equivalent to creating a lottery drawing for games using these numbers.

Where can random numbersuseful?

You might be planning an auction, a giveaway, a sweepstakes, etc. and you need to draw the winner, this generator is the perfect tool for you! It's completely impartial and completely out from your reach and therefore you can make sure your participants are assured of the fairness of the draw, which could not be so if you are using traditional methods such as rolling a dice. If you have to select more than one participant you can select the number of unique numbers you wish to see generated from our random number selector and you're good to go. However, it is usually preferred to draw the winners one at a time, so that the tension lasts longer (discarding draw after draw when you are done).

The random number generator is also beneficial if you have to determine who will be the first to play in a particular game or activity that involves board games, sport games and sports competitions. Similar to when you are required to choose the participation sequence for a number of players or participants. The selection of a team at random or randomly selecting the participants' names depends on randomness.

There are many lotteries that are run by private or government agencies as well as lottery games are using software RNGs instead of more traditional drawing methods. RNGs can also be used to determine the results of contemporary slot machines.

Finally, random numbers are also useful in simulations and statistics, where they might be generated from distributions different than the standard, e.g. an ordinary distribution, a binomial distribution and a power, the pareto distribution... In these situations, a more advanced software is required.

Making a random number

There's a philosophical dilemma regarding what exactly "random" is, but its main characteristic is surely uncertainness. It is not possible to discuss the inexplicable nature of a particular number, since that number is exactly what it is. However, we can discuss the unpredictable nature of a sequence of number (number sequence). If the sequence of numbers are random the chances are that you'll not be at a point to know the next number in the sequence while knowing the entire sequence to date. Examples of this can be seen in the game of rolling a fair die and spinning a well-balanced roulette wheel or drawing lottery balls out of a sphere, as well as the typical flip of coins. Whatever number of coins flips, dice rolls roulette spins, lottery draws you watch, you do not improve your chances of predicting the next number in the sequence. For those who are interested in physics, the best example of random motion can be seen in the Browning motion of fluid particles or gas.

Knowing that computers are 100% deterministic, meaning that their output is completely controlled by the input they receive, one might say that it is impossible to create the concept of a random number using a computer. However, this could only partially be true, as a dice roll or coin flip can also be deterministic, if you know the status of the system.

The randomness of our number generator is the result of physical processes. Our server gathers ambient noise from devices and other sources to create an entropy pool, from which random numbers are created ^[1one.

Sources of randomness

In the work of Alzhrani & Aljaedi ^{[2In the work of Alzhrani and Aljaedi [2} there are four sources of randomness that are employed in the seeding of the generator that generates random numbers, two of that are used in our number generator:

• The disk will release entropy whenever drivers request it - gathering seek time of block request events to the layer.
• Interrupting events via USB and other device drivers
• System values such as MAC addresses, serial numbers and Real Time Clock - used only to create the input pool, mostly for embedded systems.
• Entropy from input hardware - mouse and keyboard actions (not employed)

This puts the RNG that we use in this random number software in compliance with the recommendations in RFC 4086 on randomness required to protect ^[33..

True random versus pseudo random number generators

In other words, a pseudo-random-number generator (PRNG) is a finite state machine with an initial value known as the seed ^[4]. On each request, a transaction function computes the next state inside the machine, and output function outputs the exact number based on the state. A PRNG deterministically produces the periodic sequence of values that is dependent on the seed initialized. One example is an linear congruential generator such as PM88. In this way, if you know the short series of values generated,, it is possible to figure out the seed used and consequently - determine the value that will be generated next.

An Cryptographic pseudo-random generator (CPRNG) is a PRNG in that it can be identified if the internal state is known. However, assuming that the generator had been seeded with enough energy and that the algorithms have the needed properties, such generators do not immediately reveal significant amounts of their internal state, thus you'd need an immense amount of output before you can launch a successful attack against them.

A hardware RNG relies on unpredictable physical phenomenon, known as "entropy source". Radioactive decay or more precisely the timing at which a radioactive source decays is a phenomenon as close to randomness as we know, while decaying particles are easily detectable. Another example of this is heat variation Some Intel CPUs come with a detector to detect thermal noise in silicon of the chip that outputs random numbers. Hardware RNGs are, however, often biased and, more important, they are limited in their ability to produce enough entropy for practical periods of time, due to the low variability of the natural phenomena sampled. Thus, another type of RNG is needed for actual applications: an real random number generator (TRNG). In it cascades of hardware RNG (entropy harvester) are utilized to continuously replenish the PRNG. If the entropy level is enough, it behaves as an TRNG.