Random Number Generator

A Random Number Generator (RNG) is a tool or Algorithm used to produce a sequence of numbers or symbols that cannot be predicted better than by chance. RNGs are widely used in Cryptography, simulations, gaming, and various applications that require random values.

RNGs can be classified into two main categories: true Random Number Generators (TRNGs) and pseudo-Random Number Generators (PRNGs). TRNGs derive randomness from physical processes, such as electronic noise, while PRNGs use mathematical Algorithms to generate sequences that appear random but are determined by an initial seed value.

The purpose of RNGs in security and Cryptography is to generate unpredictable keys, tokens, and other values that enhance security. By ensuring values are truly random or hard to predict, RNGs make it more difficult for attackers to break Encryption schemes, guess tokens, or Exploit predictable behaviours in systems.

PRNGs work by using Algorithms and seed values to produce sequences of numbers that appear random, though they are deterministic. TRNGs, on the other hand, generate numbers based on inherently unpredictable physical processes. In cryptographic applications, PRNGs must be seeded with high-entropy values to reduce predictability. Common libraries and hardware devices are available to support secure random number generation.

Examples of RNG use include generating cryptographic keys for Encryption, creating random Session Tokens for secure logins, and simulating random events in games or simulations. In cryptographic contexts, RNGs are essential for ensuring secure, unpredictable keys and values.

RNGs, especially PRNGs, can be vulnerable to predictability if their Algorithms or seed values are weak. Attackers may Exploit flaws in RNG implementations to predict future values. To mitigate this risk, cryptographic applications should use strong Algorithms, high-entropy seed sources, and regular testing to ensure randomness and security.