How Computers generate Random Numbers – what you should know

Do you know that computers generate random numbers for everything from gaming, coding, cryptography to video games and gambling, and lots more? There are two categories of random numbers. They include “true” random numbers and pseudorandom numbers. These numbers are grouped based on how they are generated. The difference between these 2 sets of numbers is vital for the security of encryption systems.

To start with,  computers generate truly random numbers by observing some outside data, like mouse movements or fan noise, which is not predictable and creating data from it. This process is called entropy. Also, they also generate “pseudorandom” numbers by using an algorithm so the results appear random, even though they aren’t real.

What Are Random Numbers Used For?

Random number generators in a computer are used to achieve an unpredictable, random result.

These generators are used for many purposes. Apart from generating random numbers for the purposes of gambling or creating. Unpredictable results in a computer game, randomness is important for cryptography and other activities. The essence of using them for Cryptography is that it demands numbers. That attacker can’t guess, remember or even form. Without these numbers, there is no secure encryption.


True Random Numbers

Have you ever asked yourself, ‘’where does this “randomness” come from? To generate a “true” random number, the computer. Measures some type of physical phenomenon that takes place outside of the computer. For example, the computer could measure the radioactive decay of an atom. According to quantum theory, there’s no way to know for sure when. Radioactive decay will occur, so this is essentially “pure randomness” from the universe. A thief will not easily predict when. Radioactive decay will take place. It can as well use noise from a fan while the user is doing. The entries to generate them. A thief cannot know the exact moment you keyed in the numbers.

Actually, there is this /dev/random device on. Linux, generates random numbers, “blocks” and doesn’t return a result until it gets enough. Entropy to return a truly random number.

Pseudorandom Numbers

Also, Pseudorandom numbers are used as an alternative to “true” random numbers. In this case, a computer uses a seed value and an. Algorithm to generate numbers that seem to be random. However, they are predictable. Here, the computer doesn’t gather any random data from the environment.

For instance, a fraudster knows the algorithm. And seed value a pseudorandom number generator uses. And let’s say an encryption algorithm gets a pseudorandom number from this algorithm and uses it to generate an encryption key without adding any additional randomness. An attacker who knows a lot can guess to know the pseudorandom number the encryption algorithm must have chosen in that case, breaking the encryption.


The NSA and Intel’s Hardware Random Number Generator

Further, to easily generate secure random numbers, Intel chip has a hardware-based random number generator called RdRand. This chip makes use of an entropy source on the processor and gives random numbers to. Software when the software asks them.

However, the random number generator is. Primarily a black box that we cannot explain. If RdRand contained an NSA backdoor, the government. Would be able to break encryption keys that were generated with only data. Supplied by that random number generator.

In December 2013, FreeBSD’s developers removed support for using. RdRand directly as a source of randomness, saying they don’t have any confidence in it. The output of the RdRand device would be entered into. Another algorithm that adds additional entropy, ensuring that any. Backdoors in the random number generator do not have any relevance. Linux has worked like this, further randomizing the random data coming from RdRand to make sure it is not predictable even if there was a backdoor.

Lastly, to generate “true” random numbers, random. Number generators gather “entropy,” or seemingly random data from the physical. World around them. For random numbers that don’t really need to be random. Sometimes they use an algorithm and a seed value.

Now, you know how computers generate random numbers.

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