Best Practices in Generating Pseudo-Random Numbers

Pseudo Random Number Generator(PRNG) is a technique that generates random numbers. PRNGs use mathematical equations to generate the sequence of random numbers, and every PRNG is based on its own mathematical equation. The easiest method to generate random numbers is to use the built-in code libraries, which are:

• Python random()

• C-library rand(), random() and drand48()

• Standard Perl rand

• Java.util.Random

Explanation

The illustration above explains the working of PRNG algorithms and how these algorithms generate a sequence of random numbers using the seed values.

Best Practices

Build RNG in the code

The random numbers generated by the PRNG are incorrect according to the definition of true pseudo-randomness, which says that a pseudo-random number is not detectable. This is why most random number generators have failed, and we need to make our random number generator in the code.

Avoid system generators

One of the easiest ways used by developers for random numbers is by built-in libraries mentioned above. Almost all of these generators have serious flaws. Even if they aren’t, there’s no assurance that they weren’t defective in previous iterations of the library, so we should make our RNGs in the code instead of using built-in libraries.

Seed properly

Seed is a starting point to initialize the pseudo-random number generator, but if we use the seeding in a sequence, it will be predictable for the attacker. The Seed has to be unpredictable so that no one can access it.

These are the best practices that need to be followed before creating a pseudo-random number. Some well-known algorithms for generating random numbers are the Middle square method, the Xorshift algorithm, and many more.