Hackers love random numbers, or more accurately, the pursuit of them. It turns out that computers are so good at following our exacting instructions that they are largely incapable of doing anything ...

Sometimes you need random numbers — and properly random ones, at that. Hackaday Alum [Sean Boyce] whipped up a rig that serves up just that, tasty random bytes delivered fresh over MQTT. [Sean] tells ...

Quantum physics can be exploited to generate true random numbers, which have important roles in many applications, especially in cryptography. Genuine randomness from the measurement of a quantum ...

True random number generators (TRNGs) harness the inherent unpredictability of physical processes to produce random numbers crucial for cryptographic applications, secure communications, and various ...

Whether generating random device addresses, strengthening encryption algorithms or creating individual product keys, it is increasingly important to generate random numbers reliably. These random ...

In computer security, random numbers are crucial values that must be unpredictable—such as secret keys or initialization vectors (IVs)—forming the foundation of security systems. To achieve this, ...

Generating a string of random numbers is easy. The hard part is proving that they’re random. As Dilbert creator Scott Adams once pointed out, “that’s the problem with randomness: you can never be sure ...

Two University of Texas academics have made what some experts believe is a breakthrough in random number generation that could have longstanding implications for cryptography and computer security.

Lotteries, accidents and rolls of dice — the world around us is full of unpredictable events. Yet generating a truly random series of numbers for encryption has remained a surprisingly difficult task.

Learn the NumPy trick for generating synthetic data that actually behaves like real data.