Is there a true random generator?

True Random Number Generator (TRNG) At the quantum level, subatomic particles have completely random behavior, making them ideal variables of an unpredictable system. Most higher end microcontrollers have TRNG sources, which wolfSSL can use as a direct random source or as a seed for our PRNG.

wolfssl.com - True Random vs. Pseudorandom Number Generation - wolfSSL

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Pseudo Random Number Generator (PRNG)

Software-generated random numbers only are pseudorandom. They are not truly random because the computer uses an algorithm based on a distribution, and are not secure because they rely on deterministic, predictable algorithms. Since a seed number can be set to replicate the “random” numbers generated, it is possible to predict the numbers if the seed is known. Pseudorandom number generation in everyday tools such as Python and Excel are based on the Mersenne Twister algorithm. An example use of PRNGs is in key stream generation. Stream ciphers, such as Chacha, encrypt plaintext messages by applying an encryption algorithm with a pseudorandom cipher digit stream (keystream). Keystreams of some block cipher modes, such as AES CTR (counter) mode, act as a stream cipher and can also be regarded as pseudorandom number generation.

True Random Number Generator (TRNG)

For truly random numbers, the computer must use some external physical variable that is unpredictable, such as radioactive decay of isotopes or airwave static, rather than by an algorithm. At the quantum level, subatomic particles have completely random behavior, making them ideal variables of an unpredictable system. Most higher end microcontrollers have TRNG sources, which wolfSSL can use as a direct random source or as a seed for our PRNG. Intel RDRAND, a silicon-based TRNG, is supported by wolfSSL. You can find the full list of all hardware acceleration/cryptography platforms currently supported by wolfSSL here: Hardware Cryptography Support

RNGs in cryptography

However, true RNGs on their own are often not cost efficient, and can be subject to gradual decline. Thus, there is still some reliance on post-processing algorithms (that are deterministic and vulnerable) to further improve randomness, as the quality of their entropy source is not consistent. The combination of a TRNG and a PRNG can limit the negative effects of this decline. For example, in NXP i.MX RT1060, the TRNG present in the core can be used as an entropy source to determine the seed of a Deterministic Random Bit Generator (DRBG), which on its own is a PRNG, but in combination with the TRNG results in a good approximation of randomness, without weakness over time. wolfSSL uses the SHA2-256 (Secure Hash Algorithm) Hash_DRBG described in NIST’s SP 800-90A (the specification for three allegedly cryptographically secure pseudorandom number generators for use in cryptography). Additionally, wolfRand, wolfSSL’s FIPS module which includes a hardware entropy source, is conformant to NIST’s SP 800-90B (the design principles and requirements for the entropy sources used by random-bit generators, and the tests for the validation of entropy sources).

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For cryptographic purposes, a more secure approximation of a true random number can be achieved with a combination of algorithms, rather than just relying on one. In the update from TLS 1.1 to TLS 1.2, the MD5/SHA-1 combination in the pseudorandom function (PRF) was replaced with cipher-suite-specified PRFs, which continue to be used in TLS 1.3 with SHA2-256 and SHA2-384. MD5/SHA-1 (Message Digest/Secure Hash Algorithm) combined two Message Authentication Code (MAC) algorithms to provide a balance between speed and security. Meanwhile, a cipher suite is a set of cryptographic instructions or algorithms that helps secure network connections through Transport Layer Security(TLS)/Secure Socket Layer (SSL). During the SSL handshake between the web server and the client, the two parties agree on a cipher suite, which is then used to secure the HTTPS connection. A typical cipher suite contains 1 key exchange, 1 bulk encryption, 1 authentication, and 1 MAC algorithm. For more information on cipher suites and their uses, visit “What is a Cipher Suite?”

Conclusion

Truly random numbers are difficult to generate because they are not cost-efficient and subject to decline over time. However, random number generation can be made more effective by using multiple random processes in combination, either with a TRNG/PRNG combination, or an ensemble of algorithms in a cipher suite. For more information on wolfRand or general inquiries about wolfSSL, contact us at facts@wolfssl.com!

wolfssl.com - True Random vs. Pseudorandom Number Generation - wolfSSL

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