A PSEUDORANDOM SEQUENCE MEANING GENERATOR

Use the high - quality pseudo - random sequence to create pseudo - random fsk / psk signal 3.

Discuss the methods to generate pseudo - random sequences and property of them 2.

Statistical analysis of the orthogonality between images and pseudo - random sequences.

In former method, the watermark is usually the pseudo - random sequence.

A PSEUDORANDOM SEQUENCE MEANING GENERATOR

Investigation and realigation of pseudo - random sequence generator.Evaluation for uniformity of pseudo - random sequences.For example, after every data request, a further 128 bits of data could be generated, XOR'ed with any accumulated entropy available, hashed and the resulting hash output used to reseed the generator.Ĭryptographic RNGs are slower than ordinary PRNGs because they use slow cryptographic primitives and because they take extra precautions against attacks. There are various cryptographic primitives that can help with this, such as hashes. After the limit is reached, the PRNG must be reseeded from the TRNG.Īlso, the PRNG should be reseeded anyway after every data request, no matter how small. The amount of stretching allowed is limited, so the attacker never gets a long enough string of pseudo-random numbers to do any worthwhile analysis. The entropy is used to seed the PRNG and the PRNG produces more numbers based on that seed. That is where PRNGs are used to 'stretch' the real entropy to produce more pseudo random numbers from the smaller amount of entropy provided by the TRNG. A mixture of many different sources will be more resistant to attacks.Ĭommonly such sources of entropy do not produce enough random numbers to be used directly. Such a source may be a small piece of radioactive material on a card, thermal noise in a disconnected microphone circuit or other possibilities. Not for cryptographic use, as you point out.įor ideal cryptographic numbers, we need to use a true RNG (TRNG), which generates random numbers from some source of entropy (= randomness in this context). For ordinary use this is usually sufficient. You are correct, a PRNG produces a long sequence of numbers and then repeats. One detail worth noting is that there's a difference between 'the PRNG repeats a number' and 'from that point forward the numbers will always be the same.' It's possible that a PRNG will repeat an output multiple times before moving on to output a different number next, provided that the internal state is different each time. Given that most cryptographically secure PRNGs use at least 256 bits of internal storage, this makes your attack infeasible. That gives 2 n possible internal configurations of those bits, meaning that you may need to see 2 n + 1 outputs before you're guaranteed to see a repeat. Generally speaking, imagine that a pseudorandom generator uses n bits of internal storage. For example, the Mersenne twister PRNG, which isn't designed for cryptographic use, has a period of 2 19,937 - 1, which is so long that it's completely infeasible to attempt the attack that you're describing. The issue is that 'sufficiently long' might be so long that this approach is completely impractical.

You are absolutely right that in theory that approach can be used to break a PRNG, since, as you noted, given a sufficiently long sequence of outputs, you can start to predict what comes next. Lookup in the pre-generated list to find the generated number.Based on how long it takes to calculate, determine how many numbers are between the known output and the unknown one.Know the timestamp of when a private key was generated, and know the time AND -output of the PRNG later on.Use the PRNG to generate the entire looping set of numbers possible.

In the case that I am correct, if you know the algorithm for a PRNG, and that PRNG is being used for cryptography, can not this approach be used (and are there any measures in place to prevent it?): If the same seed will always give the same output when put through an algorithm, then I can be sure that the PRNG will begin a loop. If I call for an output one hundred billion and one times, I can be certain that one number has been output more than once. Let's say that I'm using a random number generator that can generate any number between 0 and one hundred billion. But here's the problem I see with it:Īny pseudorandom number generator that I can imagine has to have a finite number of outcomes. My understanding is that PRNG's work by using an input seed and an algorithm that converts it to a very unrelated output, so that the next generated number is as unpredictable as possible.

A pseudorandom sequence meaning