As digital threats continue to become more advanced, businesses and individuals alike are having to up their cybersecurity. An aspect of cybersecurity that is becoming a hot topic right now is random number generation. It is a technology that is at the heart of cryptography, while also being commonly used to generate secure passwords. Pseudo random number generators (PRNGs) have been the choice of technology until recently, but quantum random number generators (QRNGs) might be about to take over! This post explores this technology in greater detail and why it could be so important to preventing future cyberattacks.
What types of technology use random number generators?
Random number generators are used in many different forms of technology. A few examples include.
Gaming
Many video games use random number generators to create unpredictable results that can make games more engaging. Many online card games like Spider Solitaire rely on random number generation to shuffle the deck. A lot of online games also use RNGs to control computer opponent behavior – so that NPCs don’t always act the same way.
Lotteries
Many forms of gambling rely on RNGs to create fairness. While traditional lotteries still rely on the method of drawing random balls, modern lotteries often use RNGs to select winners, as well as printing codes on tickets and scratchcards.
Generative AI
A lot of generative AI uses random number generation to create unique results for each user. This is why someone can create a completely different AI-generated image using the same prompt as someone else.
Cryptography
This is the most common use for RNGs. All data transmitted across the internet is encrypted using randomly generated keys to prevent the wrong people intercepting this data. This includes email conversations, online banking data and all files stored on the cloud.
Password generation
When creating secure passwords using password generators, RNGs are typically used to create these passwords. Such passwords are often much more secure than passwords chosen personally by individuals (although can be more difficult to remember).
Why is random number generation important to cybersecurity?
Random number generation is crucial within cybersecurity because data needs to be encrypted in a way that cybercriminals cannot predict. By using algorithms to create random keys, it can prevent there from being any pattern to data encryption.
Randomly generated passwords can meanwhile be a strong defence against brute force attacks, whereby a cybercriminal will guess multiple combinations to try to break through. Using RNG technology makes the odds of a cybercriminal cracking a password much lower, because they cannot follow the typical password patterns that a human uses (such as incorporating certain words or dates).
Of course, the more complex these RNGs are, the more robust they can be. The most basic PRNGs rely on algorithms that are easy to test and determine – these are more commonly used in video games. More complex PRNGs use more elaborate algorithms that are harder to replicate – these have long been used in security until the recent introduction of QRNGs.
PRNGs vs QRNGs: What’s the difference?
There are two main types of random number generators in use today – pseudo random number generators (PRNGs) and quantum random number generators (QRNGs). PRNGs are the most traditional option, while QRNGs only reached the mass-market in 2020. Their differences are explained below:
Pseudo random number generators (PRNGs): PRNGs use predetermined algorithms to create seemingly randomized sequences of numbers and symbols. As secure as they can be, all PRNGs rely on an initial seed value. A hacker that manages to get hold of this seed can potentially predict the sequences that these RNGs produce – making this technology ultimately vulnerable and unsuitable for applications carrying critical high-value information like financial details or military information.
Quantum random number generators (QRNGs): QRNGs use quantum mechanics instead of traditional algorithms to create sequences of numbers and symbols – which makes them more authentically ‘random’. Whereas PRNGs are likely to have a certain level of repetition, QRNGs are truly unpredictable. Because they use quantum computers, they do require more energy to encrypt data and are slower than PRNGs, but are ultimately more secure. As the technology becomes cheaper to produce, they are likely to become the sole source of random number generation in security.
Conclusion
QRNGs are already being used by banks and most major software companies. As the technology develops and becomes more affordable and faster, smaller applications are certain to adopt QRNGs as a security solution. PRNGs will still have their many uses in gaming and generative AI, however we may see QRNGs being more heavily used in these fields too.