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Safe to use random numbers to make filenames unique?

I'm writing a program which basically processes data and outputs many files. There is no way it will be producing more than 10-20 files each use. I just wanted to know if using this method to generate unique filenames is a good idea? is it possible that rand will choose, lets say x and then within 10 instances, choose x again. Is using random(); a good idea? Any inputs will be appreciated!
Random rand = new Random ();
int randNo = rand.Next(100000,999999)l
using (var write = new StreamWriter("C:\\test" + randNo + ".txt")
{
// Stuff
}

I just wanted to know if using this method to generate unique filenames is a good idea?
No. Uniqueness isn't a property of randomness. Random means that the resulting value is not in any way dependent upon previous state. Which means repeats are possible. You could get the same number many times in a row (though it's unlikely).
If you want values which are unique, use a GUID:
Guid.NewGuid();
As pointed out in the comments below, this solution isn't perfect. But I contend that it's good enough for the problem at hand. The idea is that Random is designed to be random, and Guid is designed to be unique. Mathematically, "random" and "unique" are non-trivial problems to solve.
Neither of these implementations is 100% perfect at what it does. But the point is to simply use the correct one of the two for the intended functionality.
Or, to use an analogy... If you want to hammer a nail into a piece of wood, is it 100% guaranteed that the hammer will succeed in doing that? No. There exists a non-zero chance that the hammer will shatter upon contacting the nail. But I'd still reach for the hammer rather than jury rigging something with the screwdriver.

No, this is not correct method to create temporary file names in .Net.
The right way is to use either Path.GetTempFileName (creates file immediatedly) or Path.GetRandomFileName (creates high quality random name).
Note that there is not much wrong with Random, Guid.NewGuid(), DateTime.Now to generate small number of file names as covered in other answers, but using functions that are expected to be used for particular purpose leads to code that is easier to read/prove correctness.

If you want to generate a unique value, there's a tool specifically designed for generating unqiue identifying values, a Globally Unique IDentifier (GUID).
var guid = Guid.NewGuid();
Leave the problem of figuring out the best way of creating such a unique value to others.

There is what is called the Birthday Paradox... If you generate some random numbers (any number > 1), the possibility of encountering a "collision" increases... If you generate sqrt(numberofpossiblevalues) values, the possibility of a collision is around 50%... so you have 799998 possible values... sqrt(799998) is 894... It is quite low... With 45-90 calls to your program you have a 50% chance of a collision.
Note that random being random, if you generate two random numbers, there is a non-zero possibility of a collision, and if you generate numberofpossiblevalues + 1 random numbers, the possibility of a collision is 1.
Now... Someone will tell you that Guid.NewGuid will generate always unique values. They are sellers of very good snake oil. As written in the MSDN, in the Guid.NewGuid page...
The chance that the value of the new Guid will be all zeros or equal to any other Guid is very low.
The chance isn't 0, it is very (very very I'll add) low! Here the Birthday Paradox activates... Now... Microsoft Guid have 122 bits of "random" part and 6 bits of "fixed" part, the 50% chance of a collision happens around 2.3x10^18 . It is a big number! The 1% chance of collision is after 3.27x10^17... still a big number!
Note that Microsoft generates these 122 bits with a strong random number generator: https://msdn.microsoft.com/en-us/library/bb417a2c-7a58-404f-84dd-6b494ecf0d13#id9
Windows uses the cryptographic PRNG from the Cryptographic API (CAPI) and the Cryptographic API Next Generation (CNG) for generation of Version 4 GUIDs.
So while the whole Guid generated by Guid.NewGuid isn't totally random (because 6 bits are fixed), it is still quite random.

I would think it would be a good idea to add in the date & time the file was created in the file name in order to make sure it is not duplicated. You could also add random numbers to this if you want to make it even more unique (in the case your 10 files are saved at the exact same time).
So the files name might be file06182015112300.txt (showing the month, day, year, hour, minute & seconds)

If you want to use files of that format, and you know you won't run out of unused numbers, it's safer to check that the random number you generate isn't already used as follows:
Random rand = new Random();
string filename = "";
do
{
int randNo = rand.Next(100000, 999999);
filename = "C:\\test" + randNo + ".txt";
} while (File.Exists(filename));
using (var write = new StreamWriter(filename))
{
//Stuff
}

Source code for functions Rand and Randn in Matlab

I'm student from Ariel University in Israel and I'm trying to implement Matlab RAND and RANDN in C# in such way that same input for Matlab and C# (with same seed) , Randn and Rand will give the same result in both languages.
for example:
In Matlab:
rand('seed',123)
disp(rand)
output: 0.0878
In C#:
Console.WriteLine(MyRand(123));
output: 0.0878
I think for implement this kind of functionality, I need to have the source code for RAND and RANDN in Matlab. Does anyone has this code and may share?
Thanks a lot,
Shimon

Doing:
>> s = RandStream.getGlobalStream()
s =
mt19937ar random stream (current global stream)
Seed: 0
NormalTransform: Ziggurat
Your given the random-number-generator algorithm and the transformation used to get normal distributed numbers.
Both are publicly available algorithms.
Google gives you e.g.:
http://www.math.sci.hiroshima-u.ac.jp/~%20m-mat/MT/MT2002/emt19937ar.html
and
http://www.jstatsoft.org/v05/i08/paper
describing both algorithms including reference / example implemenations.

Randn is as far as i know MarsenneTwister. To verify this i would first try to use the MarsenneTwister from Apache and check for similar results: http://commons.apache.org/proper/commons-math/apidocs/org/apache/commons/math3/random/MersenneTwister.html
If so: Search for any implementation. This algorithm should be documented.

But seriously, if you type
edit rand.m
into the Matlab command window, and
edit randn.m
I think you will get as much information as the Mathworks publish about those functions. This information points towards the algorithms used and, for rand an implementation too.

As your question only mentions obtaining the same results, I would recommend one of the following:
Generate a lot of random numbers, then use them one by one in both programming languages.
Implement your own (simple) random generator in both languages.

How is a random number generated at runtime?

Since computers cannot pick random numbers(can they?) how is this random number actually generated. For example in C# we say,
Random.Next()
What happens inside?

You may checkout this article. According to the documentation the specific implementation used in .NET is based on Donald E. Knuth's subtractive random number generator algorithm. For more information, see D. E. Knuth. "The Art of Computer Programming, volume 2: Seminumerical Algorithms". Addison-Wesley, Reading, MA, second edition, 1981.

Since computers cannot pick random numbers (can they?)
As others have noted, "Random" is actually pseudo-random. To answer your parenthetical question: yes, computers can pick truly random numbers. Doing so is much more expensive than the simple integer arithmetic of a pseudo-random number generator, and usually not required. However there are applications where you must have non-predictable true randomness: cryptography and online poker immediately come to mind. If either use a predictable source of pseudo-randomness then attackers can decrypt/forge messages much more easily, and cheaters can figure out who has what in their hands.
The .NET crypto classes have methods that give random numbers suitable for cryptography or games where money is on the line. As for how they work: the literature on crypto-strength randomness is extensive; consult any good university undergrad textbook on cryptography for details.
Specialty hardware also exists to get random bits. If you need random numbers that are drawn from atmospheric noise, see www.random.org.

Knuth covers the topic of randomness very well.
We don't really understand random well. How can something predictable be random? And yet pseudo-random sequences can appear to be perfectly random by statistical tests.
There are three categories of Random generators, amplifying on the comment above.
First, you have pseudo random number generators where if you know the current random number, it's easy to compute the next one. This makes it easy to reverse engineer other numbers if you find out a few.
Then, there are cryptographic algorithms that make this much harder. I believe they still are pseudo random sequences (contrary to what the comment above implies), but with the very important property that knowing a few numbers in the sequence does NOT make it obvious how to compute the rest. The way it works is that crypto routines tend to hash up the number, so that if one bit changes, every bit is equally likely to change as a result.
Consider a simple modulo generator (similar to some implementations in C rand() )
int rand() {
return seed = seed * m + a;
}
if m=0 and a=0, this is a lousy generator with period 1: 0, 0, 0, 0, ....
if m=1 and a=1, it's also not very random looking: 0, 1, 2, 3, 4, 5, 6, ...
But if you pick m and a to be prime numbers around 2^16, this will jump around nicely looking very random if you are casually inspecting. But because both numbers are odd, you would see that the low bit would toggle, ie the number is alternately odd and even. Not a great random number generator. And since there are only 2^32 values in a 32 bit number, by definition after 2^32 iterations at most, you will repeat the sequence again, making it obvious that the generator is NOT random.
If you think of the middle bits as nice and scrambled, while the lower ones aren't as random, then you can construct a better random number generator out of a few of these, with the various bits XORed together so that all the bits are covered well. Something like:
(rand1() >> 8) ^ rand2() ^ (rand3() > 5) ...
Still, every number is flipping in synch, which makes this predictable. And if you get two sequential values they are correlated, so that if you plot them you will get lines on your screen. Now imagine you have rules combining the generators, so that sequential values are not the next ones.
For example
v1 = rand1() >> 8 ^ rand2() ...
v2 = rand2() >> 8 ^ rand5() ..
and imagine that the seeds don't always advance. Now you're starting to make something that's much harder to predict based on reverse engineering, and the sequence is longer.
For example, if you compute rand1() every time, but only advance the seed in rand2() every 3rd time, a generator combining them might not repeat for far longer than the period of either one.
Now imagine that you pump your (fairly predictable) modulo-type random number generator through DES or some other encryption algorithm. That will scramble up the bits.
Obviously, there are better algorithms, but this gives you an idea. Numerical Recipes has a lot of algorithms implemented in code and explained. One very good trick: generate not one but a block of random values in a table. Then use an independent random number generator to pick one of the generated numbers, generate a new one and replace it. This breaks up any correlation between adjacent pairs of numbers.
The third category is actual hardware-based random number generators, for example based on atmospheric noise
http://www.random.org/randomness/
This is, according to current science, truly random. Perhaps someday we will discover that it obeys some underlying rule, but currently, we cannot predict these values, and they are "truly" random as far as we are concerned.
The boost library has excellent C++ implementations of Fibonacci generators, the reigning kings of pseudo-random sequences if you want to see some source code.

I'll just add an answer to the first part of the question (the "can they?" part).h
Computers can generate (well, generate may not be an entirely accurate word) random numbers (as in, not pseudo-random). Specifically, by using environmental randomness which is gotten through specialized hardware devices (that generates randomness based on noise, for e.g.) or by using environmental inputs (e.g. hard disk timings, user input event timings).
However, that has no bearing on the second question (which was how Random.Next() works).

The Random class is a pseudo-random number generator.
It is basically an extremely long but deterministic repeating sequence. The "randomness" comes from starting at different positions. Specifying where to start is done by choosing a seed for the random number generator and can for example be done by using the system time or by getting a random seed from another random source. The default Random constructor uses the system time as a seed.
The actual algorithm used to generate the sequence of numbers is documented in MSDN:
The current implementation of the Random class is based on Donald E. Knuth's subtractive random number generator algorithm. For more information, see D. E. Knuth. "The Art of Computer Programming, volume 2: Seminumerical Algorithms". Addison-Wesley, Reading, MA, second edition, 1981.

Computers use pseudorandom number generators. Essentially, they work by start with a seed number and iterating it through an algorithm each time a new pseudorandom number is required.
The process is of course entirely deterministic, so a given seed will generate exactly the same sequence of numbers every time it is used, but the numbers generated form a statistically uniform distribution (approximately), and this is fine, since in most scenarios all you need is stochastic randomness.
The usual practice is to use the current system time as a seed, though if more security is required, "entropy" may be gathered from a physical source such as disk latency in order to generate a seed that is more difficult to predict. In this case, you'd also want to use a cryptographically strong random number generator such as this.

I don't know much details but what I know is that a seed is used in order to generate the random numbers it is then based on some algorithm that uses that seed that a new number is obtained.
If you get random numbers based on the same seed they will be the same often.

How can I generate truly (not pseudo) random numbers with C#?

I know that the Random class can generate pseudo-random numbers but is there a way to generate truly random numbers?

The answer here has two main sides to it. There are some quite important subtleties to which you should pay due attention...
The Easy Way (for simplicity & practicality)
The RNGCryptoServiceProvider, which is part of the Crypto API in the BCL, should do the job for you. It's still technically a pseudo-random number generated, but the quality of "randomness" is much higher - suitable for cryptographic purposes, as the name might suggest.
There are other crypographic APIs with high quality pseudo random generaters available too. Algorithms such as the Mersenne twister are quite popular.
Comparing this to the Random class in the BCL, it is significantly better. If you plot the numbers generated by Random on a graph, for example, you should be able to recognise patterns, which is a strong sign of weakness. This is largely due to the fact that the algorithm simply uses a seeded lookup table of fixed size.
The Hard Way (for high quality theoretical randomness)
To generate truly random numbers, you need to make use of some natural phenomenon, such as nuclear decay, microscopic temperature fluctuations (CPU temperature is a comparatively conveient source), to name a few. This however is much more difficult and requires additional hardware, of course. I suspect the practical solution (RNGCryptoServiceProvider or such) should do the job perfectly well for you.
Now, note that if you really do require truly random numbers, you could use a service such as Random.org, which generates numbers with very high randomness/entropy (based on atmospheric noise). Data is freely available for download. This may nonetheless be unnecessarily complicated for your situation, although it certainly gives you data suitable for scientific study and whatnot.
The choice is yours in the end, but at least you should now be able to make an informative decision, being aware of the various types and levels of RNGs.

short answer: It is not directly possible to generate TRULY RANDOM NUMBERS using only C# (i.e. using only a purely mathematical construction).
long(er) answer: Only by means of employing an external device capable of generating "randomness" such as a white noise generator or similar - and capturing the output of that device as a seed for a pseudo random number generator (PRG). That part could be accomplished using C#.

True random numbers can only be generated if there is a truly random physical input device that provides the seed for the random function.
Whether anything physical and truly random exists is still debated (and likely will be for a long time) by the science community.
Psuedo-random number generators are the next best thing and the best are very difficult to predict.

As John von Neumann joked, "Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin."

The thread is old and answered, but i thought I'd proceed anyway. It's for completeness and people should know some things about Random in c#.
As for truly random, the best you can ever hope to do is use a "secure Pseudo Random Generator" like salsa20 or RC4 (sort of, sometimes). They pass a barrage of tests where "efficient" adversaries try to distinguish them from random. This comes with certain costs and is probably unnecessary for most uses.
The random class in c# is pretty good most of the time, it has a statically distribution that looks random. However the default seed for random() is the system time. So if you take lots of randoms at the "same time" they are taken with the same seed and will be the same ("random" is completely deterministic, don't let it fool you). Similar system time seeds also may produce similar numbers because of random class's shortcomings.
The way to deal with this is to set you own seeds, like
Random random = new Random((int)DateTime.Now.Ticks & (0x0000FFFF + x));
where x is some value you increment if you've created a loop to get a bunch of random numbers, say.
Also with c# random extensionsto your new variable like NextDouble() can be helpful in manipulating the random numbers, in this case crow-baring them into interval (0,1) to become unif(0,1), which happens is a distribution you can plug into stat formulas to create all the distributions in statistics.

Take a look at using an algorithm like Yarrow or Fortuna with entropy accumulation. The point with these algorithms is that they keep track of entropy as a measure of theoretical information content available for predicting future numbers by knowing the past numbers and the algorithms used to produce them; and they use cryptographic techniques to fold new entropy sources into the number generator.
You'll still need an external source of random data (e.g. hardware source of random numbers), whether that's time of keystrokes, or mouse movement, or hard disk access times, or CPU temperature, or webcam data, or stock prices, or whatever -- but in any case, you keep mixing this information into the entropy pools, so that even if the truly random data is slow or low quality, it's enough to keep things going in an unpredictable fashion.

I was debating building a random number generator based off twitter or one of the other social networking sites. Basically use the api to pull recent posts and then use that to seed a high quality pseudo random number generator. It probably isn't any more effective than randomizing off the timer but seemed like fun. Besides it seems like the best use for most of the stuff people post to twitter.

I always liked this idea, for the retro 60s look:
Lavarand

There is no "true" random in computers, everything is based on something else. For some (feasible) ways to generate pseudorandom data, try something such as a pool of the HD temp, CPU temp, network usage (packets/second) and possibly hits/second to the webserver.

Just to clarify everyone saying that there is no True RNG available in C# or on your computer is mistaken. A multi-core processor is inherently a True RNG. Very simply by taking advantage of processor spin you can generate bools that have no discernible pattern. From there you can generate whatever number range you want by using the bools as bits and constructing the number by adding the bits together.
Yes this is magnitudes slower than a purely mathematical solution but a purely mathematical solution will always have a pattern.
public static bool GenerateBoolean()
{
var gen1 = 0;
var gen2 = 0;
Task.Run(() =>
{
while (gen1 < 1 || gen2 < 1)
Interlocked.Increment(ref gen1);
});
while (gen1 < 1 || gen2 < 1)
Interlocked.Increment(ref gen2);
return (gen1 + gen2) % 2 == 0;
}

There is no way to generate truly random numbers with a computer. True randomness requires an external source that monitors some natural phenomenon.
That said, if you don't have access to such a source of truly random numbers you could use a "poor man's" process like this:
Create a long array (10000 or more items?) of numbers
Populate the array with current time-seeded random numbers the standard way
When a random number is required, generate a random index into the array and return the number contained at that position
Create a new, current time-seeded random number at the array index to replace the number used
This two-step process should improve the randomness of your results somewhat without the need for external input.
Here's a sample library that implements the above-described algorithm in C++: http://www.boost.org/doc/libs/1_39_0/libs/random/random-generators.html

This code will return you a random number between min and max:
private static readonly Random random = new Random();
private static readonly object syncLock = new object();
public int RandomNumber(int min, int max)
{
lock (syncLock)
{ // synchronize
return random.Next(min, max);
}
}
Usage:
int randomNumber = RandomNumber(0, 10); // a random number between 1 and 10

Seeding a random number generator in .NET

I have a pseudorandom number generator (PRNG) with nice properties which uses six UInt32s as state. I need to come up with a reasonable way to seed it. Two obvious possibilities are: 1) generate six random numbers using System.Random and use them as seeds; 2) generate two GUIDs with Guid.NewGuid(). Which would be better?
I do not need cryptographic security.

If it needs UInt32, then Random is more convenient? just Next(), Next(), Next() etc (and cast)... (use the same Random instance however - don't create new Random() each time).
It depends on what the intent is as to whether this offers enough randomness. Since this is just the seed, it should be OK...

Unfortunately System.Random() also requires a seed value. By default it uses the current Tick count which is predictable and not actually random. So you'll need a seed for Random which leads you back to your original question ...
I haven't ever used Guid.GetHashCode() as a seed before but my 2 second reaction is that doesn't sound like a bad idea.

Whether or not you need cryptographic security, why not just use System.Security.Cryptography.RNGCryptoServiceProvider to generate your random numbers? Unless there's a specific reason, like it's too slow, I can't see why you wouldn't use it. Since it is a cryptographic random generator, you'll get much better random numbers, and don't have to be worried about seeding it.

Try this for your seed value...
(UInt32)Math.Pow(System.DateTime.Now.TimeOfDay.TotalMilliseconds, 11.0 / 7.0)
It just raises the current time's milliseconds to the 11/7th power, which is just arbitrary.
You can experiment with other fractions to see if they work better for you.
Beware that if your fraction's decimal equivalent is greater than about 2.5, you might get an overflow and your seed value will be zero. :(
I've used this for awhile and it seems to give pretty good seed values.