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Deterministic and secure random number generator in C# [duplicate]

It seems that there is no way to manually seed the RNGCryptoServiceProvider in C#. Is there really nothing simple I can do below to get repeatable randomBytes here for debugging?
RNGCryptoServiceProvider rngCsp = new RNGCryptoServiceProvider();
byte[] randomBytes = new byte[20];
rngCsp.GetBytes(randomBytes);
MessageBox.Show(Convert.ToBase64String(randomBytes));
I know that I could manually enter the 20 bytes, but this is a pain since I really need many more than 20. Also, I know that I could use the non-cryptography random number generator, but, in the end, I will need the best random generation.
By the way, I would guess some CPUs have true random generation built in where seeding is not physically possible, but I don't think my CPU has this capability. I wonder if anybody knows exactly what I could do with my CPU to reset the RNGCryptoServiceProvider environment and trick RNGCryptoServiceProvider into using a prior seed...I imagine I could set my clock back and reset some "user log bits" somewhere...I know this wouldn't be practical, but wonder if anybody has ever been successful at this (even though Microsoft's goal is probably to prevent this).

There is not a means to seed the RNGCryptoServiceProvider. One solution to generating deterministic values for debugging is to derive your own class that implements from System.Security.Cryptography.RandomNumberGenerator (the base class for RNGCryptoServiceProvider):
class DeterministicRandomGenerator : System.Security.Cryptography.RandomNumberGenerator
{
Random r = new Random(0);
public override void GetBytes(byte[] data)
{
r.NextBytes(data);
}
public override void GetNonZeroBytes(byte[] data)
{
// simple implementation
for (int i = 0; i < data.Length; i++)
data[i] = (byte)r.Next(1, 256);
}
}
Note the implementation uses the standard Random implementation with a seed of 0, ensuring deterministic results. Now you can use this class in place of RNGCryptoServiceProvider for debugging purposes:
RandomNumberGenerator rngCsp =
#if DEBUG
new DeterministicRandomGenerator(); // get deterministic values if debugging
#else
new RNGCryptoServiceProvider(); // otherwise, use CryptoRNG
#endif
byte[] randomBytes = new byte[20];
rngCsp.GetBytes(randomBytes);
MessageBox.Show(Convert.ToBase64String(randomBytes));
Edited to add
I wonder if anybody knows exactly what I could do with my CPU to reset the RNGCryptoServiceProvider environment and trick RNGCryptoServiceProvider into using a prior seed
Internally, RNGCryptoServiceProvider calls the Win32 CryptGenRandom function to fill the buffer with cryptographically-random values (Source and additional information). It is not based on a single seed. (Although the Win32 API allows the caller to provide a seed with supplemental random data, the .NET API does not expose this functionality. The purpose of a seed in this context is to provide additional entropy the application has access to, rather than to force a deterministic sequence.) The CryptGenRandom documentation states that:
To form the seed for the random number generator, a calling application supplies bits it might have—for instance, mouse or keyboard timing input—that are then combined with both the stored seed and various system data and user data such as the process ID and thread ID, the system clock, the system time, the system counter, memory status, free disk clusters, the hashed user environment block. This result is used to seed the pseudorandom number generator (PRNG). In Windows Vista with Service Pack 1 (SP1) and later, an implementation of the AES counter-mode based PRNG specified in NIST Special Publication 800-90 is used. In Windows Vista, Windows Storage Server 2003, and Windows XP, the PRNG specified in Federal Information Processing Standard (FIPS) 186-2 is used.
The result is that "resetting" the RNGCryptoServiceProvider to coerce it into repeating a former sequence is, by design, not a practical approach.

Also, I know that I could use the non-cryptography random number generator, but, in the end, I will need the best random generation.
Use a Mersenne Twister or similar as a seedable PRNG for testing purposes - only in those tests where a repeatable result is required - and the RCPCryptoServiceProvider for everything else.
Another alternative is to pre-generate a batch of randomBytes arrays and save those to disk for use in your testing phase. This gives the advantage of giving you repeatability while retaining the same random distribution and other characteristics of the RCP CSP. Also it's easier to forget to remove temporary code than it is to forget NOT to include extraneous files.

Guid.NewGuid() VS a random string generator from Random.Next()

My colleague and I are debating which of these methods to use for auto generating user ID's and post ID's for identification in the database:
One option uses a single instance of Random, and takes some useful parameters so it can be reused for all sorts of string-gen cases (i.e. from 4 digit numeric pins to 20 digit alphanumeric ids). Here's the code:
// This is created once for the lifetime of the server instance
class RandomStringGenerator
{
public const string ALPHANUMERIC_CAPS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890";
public const string ALPHA_CAPS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ";
public const string NUMERIC = "1234567890";
Random rand = new Random();
public string GetRandomString(int length, params char[] chars)
{
string s = "";
for (int i = 0; i < length; i++)
s += chars[rand.Next() % chars.Length];
return s;
}
}
and the other option is simply to use:
Guid.NewGuid();
see Guid.NewGuid on MSDN
We're both aware that Guid.NewGuid() would work for our needs, but I would rather use the custom method. It does the same thing but with more control.
My colleague thinks that because the custom method has been cooked up ourselves, it's more likely to generate collisions. I'll admit I'm not fully aware of the implementation of Random, but I presume it is just as random as Guid.NewGuid(). A typical usage of the custom method might be:
RandomStringGenerator stringGen = new RandomStringGenerator();
string id = stringGen.GetRandomString(20, RandomStringGenerator.ALPHANUMERIC_CAPS.ToCharArray());
Edit 1:
We are using Azure Tables which doesn't have an auto increment (or similar) feature for generating keys.
Some answers here just tell me to use NewGuid() "because that's what it's made for". I'm looking for a more in depth reason as to why the cooked up method may be more likely to generate collisions given the same degrees of freedom as a Guid.
Edit 2:
We were also using the cooked up method to generate post ID's which, unlike session tokens, need to look pretty for display in the url of our website (like http://mywebsite.com/14983336), so guids are not an option here, however collisions are still to be avoided.

I am looking for a more in depth reason as to why the cooked up method may be more likely to generate collisions given the same degrees of freedom as a Guid.
First, as others have noted, Random is not thread-safe; using it from multiple threads can cause it to corrupt its internal data structures so that it always produces the same sequence.
Second, Random is seeded based on the current time. Two instances of Random created within the same millisecond (recall that a millisecond is several million processor cycles on modern hardware) will have the same seed, and therefore will produce the same sequence.
Third, I lied. Random is not seeded based on the current time; it is seeded based on the amount of time the machine has been active. The seed is a 32 bit number, and since the granularity is in milliseconds, that's only a few weeks until it wraps around. But that's not the problem; the problem is: the time period in which you create that instance of Random is highly likely to be within a few minutes of the machine booting up. Every time you power-cycle a machine, or bring a new machine online in a cluster, there is a small window in which instances of Random are created, and the more that happens, the greater the odds are that you'll get a seed that you had before.
(UPDATE: Newer versions of the .NET framework have mitigated some of these problems; in those versions you no longer have every Random created within the same millisecond have the same seed. However there are still many problems with Random; always remember that it is only pseudo-random, not crypto-strength random. Random is actually very predictable, so if you are relying on unpredictability, it is not suitable.)
As other have said: if you want a primary key for your database then have the database generate you a primary key; let the database do its job. If you want a globally unique identifier then use a guid; that's what they're for.
And finally, if you are interested in learning more about the uses and abuses of guids then you might want to read my "guid guide" series; part one is here:
https://ericlippert.com/2012/04/24/guid-guide-part-one/

As written in other answers, my implementation had a few severe problems:
Thread safety: Random is not thread safe.
Predictability: the method couldn't be used for security critical identifiers like session tokens due to the nature of the Random class.
Collisions: Even though the method created 20 'random' numbers, the probability of a collision is not (number of possible chars)^20 due to the seed value only being 31 bits, and coming from a bad source. Given the same seed, any length of sequence will be the same.
Guid.NewGuid() would be fine, except we don't want to use ugly GUIDs in urls and .NETs NewGuid() algorithm is not known to be cryptographically secure for use in session tokens - it might give predictable results if a little information is known.
Here is the code we're using now, it is secure, flexible and as far as I know it's very unlikely to create collisions if given enough length and character choice:
class RandomStringGenerator
{
RNGCryptoServiceProvider rand = new RNGCryptoServiceProvider();
public string GetRandomString(int length, params char[] chars)
{
string s = "";
for (int i = 0; i < length; i++)
{
byte[] intBytes = new byte[4];
rand.GetBytes(intBytes);
uint randomInt = BitConverter.ToUInt32(intBytes, 0);
s += chars[randomInt % chars.Length];
}
return s;
}
}

"Auto generating user ids and post ids for identification in the database"...why not use a database sequence or identity to generate keys?
To me your question is really, "What is the best way to generate a primary key in my database?" If that is the case, you should use the conventional tool of the database which will either be a sequence or identity. These have benefits over generated strings.
Sequences/identity index better. There are numerous articles and blog posts that explain why GUIDs and so forth make poor indexes.
They are guaranteed to be unique within the table
They can be safely generated by concurrent inserts without collision
They are simple to implement
I guess my next question is, what reasons are you considering GUID's or generated strings? Will you be integrating across distributed databases? If not, you should ask yourself if you are solving a problem that doesn't exist.

Your custom method has two problems:
It uses a global instance of Random, but doesn't use locking. => Multi threaded access can corrupt its state. After which the output will suck even more than it already does.
It uses a predictable 31 bit seed. This has two consequences:
You can't use it for anything security related where unguessability is important
The small seed (31 bits) can reduce the quality of your numbers. For example if you create multiple instances of Random at the same time(since system startup) they'll probably create the same sequence of random numbers.
This means you cannot rely on the output of Random being unique, no matter how long it is.
I recommend using a CSPRNG (RNGCryptoServiceProvider) even if you don't need security. Its performance is still acceptable for most uses, and I'd trust the quality of its random numbers over Random. If you you want uniqueness, I recommend getting numbers with around 128 bits.
To generate random strings using RNGCryptoServiceProvider you can take a look at my answer to How can I generate random 8 character, alphanumeric strings in C#?.
Nowadays GUIDs returned by Guid.NewGuid() are version 4 GUIDs. They are generated from a PRNG, so they have pretty similar properties to generating a random 122 bit number (the remaining 6 bits are fixed). Its entropy source has much higher quality than what Random uses, but it's not guaranteed to be cryptographically secure.
But the generation algorithm can change at any time, so you can't rely on that. For example in the past the Windows GUID generation algorithm changed from v1 (based on MAC + timestamp) to v4 (random).

Use System.Guid as it:
...can be used across all computers and networks wherever a unique identifier is required.
Note that Random is a pseudo-random number generator. It is not truly random, nor unique. It has only 32-bits of value to work with, compared to the 128-bit GUID.
However, even GUIDs can have collisions (although the chances are really slim), so you should use the database's own features to give you a unique identifier (e.g. the autoincrement ID column). Also, you cannot easily turn a GUID into a 4 or 20 (alpha)numeric number.

Contrary to what some people have said in the comment, a GUID generated by Guid.NewGuid() is NOT dependent on any machine-specific identifier (only type 1 GUIDs are, Guid.NewGuid() returns a type 4 GUID, which is mostly random).
As long as you don't need cryptographic security, the Random class should be good enough, but if you want to be extra safe, use System.Security.Cryptography.RandomNumberGenerator. For the Guid approach, note that not all digits in a GUID are random. Quote from wikipedia:
In the canonical representation, xxxxxxxx-xxxx-Mxxx-Nxxx-xxxxxxxxxxxx, the most significant bits of N indicates the variant (depending on the variant; one, two or three bits are used). The variant covered by the UUID specification is indicated by the two most significant bits of N being 1 0 (i.e. the hexadecimal N will always be 8, 9, A, or B).
In the variant covered by the UUID specification, there are five versions. For this variant, the four bits of M indicates the UUID version (i.e. the hexadecimal M will either be 1, 2, 3, 4, or 5).

Regarding your edit, here is one reason to prefer a GUID over a generated string:
The native storage for a GUID (uniqueidentifier) in SQL Server is 16 bytes. To store a equivalent-length varchar (string), where each "digit" in the id is stored as a character, would require somewhere between 32 and 38 bytes, depending on formatting.
Because of its storage, SQL Server is also able to index a uniqueidentifier column more efficiently than a varchar column as well.

What types of attacks are possible against this password generation scheme?

The code that follows uses the PRNG (pseudo random number generator) Random class to generate password characters for the initial temporary password instead of the much more cryptographically secure RNGCryptoServiceProvider as it should have used.
However, it does use the RNGCryptoServiceProvider to generate a seed for the PRNG, so I'm thinking that's maybe worth something, instead of seeding based on the current time of day as is typical practice when using a PRNG where security is not a concern.
My question is: how easy or difficult is this approach to attack in order to compromise the password generation system and guess new users' passwords?
// Generate 4 random bytes.
byte[] randomBytes = new byte[4];
RNGCryptoServiceProvider rng = new RNGCryptoServiceProvider();
rng.GetBytes(randomBytes);
// Convert 4 bytes into a 32-bit integer value.
int seed = (randomBytes[0] & 0x7f) << 24 | randomBytes[1] << 16 | randomBytes[2] << 8 | randomBytes[3];
// Now, this is real randomization.
Random random = new Random(seed);
The code then goes on to use random.Next() to generate characters to fill in the password string.
DISCLAIMER: This code is not of my invention. Do not blame me for it nor offer suggestions on how to fix it. I know how to fix it and I know it is bad. Do not waste time replying as such. Any comments or answers to this effect will be flagged as spam. I only found it in our code and am curious about its "security" properties.

The issue with PRNG functions is that of predictability. Being able to predict it's output based on previous output. The reason to avoid using the Random class is that by monitoring it's output, one can then start to predict future output.
The code above may or may not be a problem. This boils down to how often the Random class is instantiated. If you are creating a new instance with a seed from a crypto-strength PRNG and generating only a single password from that then you should be OK. I say this because even if I learn the state of the PRNG from one generated password, it has no relationship to future passwords generated.
If you instead are using this routine to initialize a static instance of Random, then you certainly have a potential problem. Let's say someone used this approach to send temporary reset passwords to an email. An attacker could reset their own password enough times to start predicting the future passwords. Once he can predict the next password, he simply initiate the rest for the account he wishes to compromise. Already knowing the password that was emailed, he can then access the account.

How to seed GUID generation?

What would be the easiest way to code a function in .NET to generate a GUID based on a seed so that I can have greater confidence about its uniqueness?
string GenerateSeededGuid(int seed) { /* code here */ }
Ideally, the seed would come from CryptGenRandom which describes its random number generation as follows:
The data produced by this function is cryptographically random. It is
far more random than the data generated by the typical random number
generator such as the one shipped with your C compiler.
This function is often used to generate random initialization vectors
and salt values.
Software random number generators work in fundamentally the same way.
They start with a random number, known as the seed, and then use an
algorithm to generate a pseudo-random sequence of bits based on it.
The most difficult part of this process is to get a seed that is truly
random. This is usually based on user input latency, or the jitter
from one or more hardware components.
With Microsoft CSPs, CryptGenRandom uses the same random number
generator used by other security components. This allows numerous
processes to contribute to a system-wide seed. CryptoAPI stores an
intermediate random seed with every user. To form the seed for the
random number generator, a calling application supplies bits it might
have—for instance, mouse or keyboard timing input—that are then
combined with both the stored seed and various system data and user
data such as the process ID and thread ID, the system clock, the
system time, the system counter, memory status, free disk clusters,
the hashed user environment block. This result is used to seed the
pseudorandom number generator (PRNG). [...] If an application has access to a good random source, it can
fill the pbBuffer buffer with some random data before calling
CryptGenRandom. The CSP then uses this data to further randomize its
internal seed. It is acceptable to omit the step of initializing the
pbBuffer buffer before calling CryptGenRandom.

tldr; use Guid.NewGuid instead of trying to invent another "more random" approach. (The only reason I can think of to create a UUIDvX from a seed is when a predictable, resettable, sequence is desired. However, a GUID might also not be the best approach2.)
By very definition of being a finite range - 128bits minus 6 versioning bits, so 122 bits of uniqueness for v4 - there are only so many (albeit supremely huge number! astronomically big!) "unique" identifiers.
Due to the Pigeonhole Principle there are only so many Pigeonholes. If Pigeons keep reproducing eventually there will not be enough Holes for each Pigeon. Due to the Birthday Paradox, assuming complete randomness, two Pigeons will try to fight for the same Pigeonholes before they are all filled up. Because there is no Master Pigeonhole List1 this cannot be prevented. Also, not all animals are Pigeons3.
While there are no guarantees as to which GUID generator will be used, .NET uses the underlying OS call, which is a GUIDv4 (aka Random UUID) generator since Windows 2k. As far as I know - or care, really - this is as good a random as it gets for such a purpose. It has been well vetted for over a decade and has not been replaced.
From Wikipedia:
.. only after generating 1 billion UUIDs every second for the next 100 years, the probability of creating just one duplicate would be about 50%. The probability of one duplicate would be about 50% if every person on earth owns 600 million UUIDs.
1 While there are still a finite set of Pigeonholes, UUIDv1 (aka MAC UUID) - assuming unique time-space - is guaranteed to generate deterministically unique numbers (with some "relatively small" theoretical maximum number of UUIDs generated per second on a given machine). Different broods of Pigeons living in different parallel dimensions - awesome!
2 Twitter uses Snowflakes in parallel dimensions in its own distributed Unique-ID scheme.
3 Rabbits like to live in Burrows, not Pigeonholes. The use of a GUID also acts as an implicit parallel partition. It is only when a duplicate GUID is used for the same purpose that collision-related problems can arise. Just think of how many duplicate auto-increment database primary keys there are!

All you really need to do in your GenerateSeededGuid method is to create a 128-bit random number and the convert it to a Guid. Something like:
public Guid GenerateSeededGuid(int seed)
{
var r = new Random(seed);
var guid = new byte[16];
r.NextBytes(guid);
return new Guid(guid);
}

This is a bit old, but no need for a random generator. But yes this is usefull for testing purpose, but not for general uses
public static Guid GenerateSeededGuid<T>(T value)
{
byte[] bytes = new byte[16];
BitConverter.GetBytes(value.GetHashCode()).CopyTo(bytes, 0);
return new Guid(bytes);
}

public static Guid SeededGuid(int seed, Random random = null)
{
random ??= new Random(seed);
return Guid.Parse(string.Format("{0:X4}{1:X4}-{2:X4}-{3:X4}-{4:X4}-{5:X4}{6:X4}{7:X4}",
random.Next(0, 0xffff), random.Next(0, 0xffff),
random.Next(0, 0xffff),
random.Next(0, 0xffff) | 0x4000,
random.Next(0, 0x3fff) | 0x8000,
random.Next(0, 0xffff), random.Next(0, 0xffff), random.Next(0, 0xffff)));
}
//Example 1
SeededGuid("Test".GetHashCode());
SeededGuid("Test".GetHashCode());
//Example 2
var random = new Random("Test".GetHashCode());
SeededGuid("Test".GetHashCode(), random);
SeededGuid("Test".GetHashCode(), random);
This method is based on php v4 uui https://www.php.net/manual/en/function.uniqid.php#94959

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