The file /dev/random has major device number 1 and minor device number 8. The file /dev/urandom has major device number 1 and minor device number 9. The random number generator gathers environmental noise from device drivers and other sources into an entropy pool. Redistributions of source code must retain the above copyright. notice. mknod /dev/random c 1 8. mknod /dev/urandom c 1 9. Acknowledgements. A read(2) from /dev/random will return at most 512 bytes (340 bytes on Linux kernels before version 2.6.12). Writing to /dev/random or /dev/urandom will update the entropy pool with the data written, but this will not result.

C program to generate pseudo-random numbers using rand and random function (Turbo C compiler only). As the random numbers are generated by an algorithm used in a function they are pseudo-random, this is the reason that word pseudo is used. Function rand() returns a pseudo-random number between 0 and RAND_MAX. RAND_MAX is a constant which is platform dependent and equals the maximum value returned by rand function.

C programming code using rand

We use modulus operator in our program. If you evaluate a % b where a and b are integers then result will always be less than b for any set of values of a and b. For example
For a = 1243 and b = 100
a % b = 1243 % 100 = 43
For a = 99 and b = 100
a % b = 99 % 100 = 99
For a = 1000 and b = 100
a % b = 1000 % 100 = 0

In our program we print pseudo random numbers in range [0, 100]. So we calculate rand() % 100 which will return a number in [0, 99] so we add 1 to get the desired range.

int main(){
int c, n;

printf('Ten random numbers in [1,100]n');

for(c =1; c <=10; c++){
n =rand()%100+1;
printf('%dn', n);
}

return0;
}

If you rerun this program, you will get the same set of numbers. To get different numbers every time you can use: srand(unsigned int seed) function; here seed is an unsigned integer. So you will need a different value of seed every time you run the program for that you can use current time which will always be different so you will get a different set of numbers. By default, seed = 1 if you do not use srand function.

C programming code using random function (Turbo C compiler only)

Function randomize is used to initialize random number generator. If you don't use it, then you will get same random numbers each time you run the program.

int main()
{
int n, max, num, c;

printf('Enter the number of random numbers you wantn');
scanf('%d',&n);

printf('Enter the maximum value of random numbern');
scanf('%d',&max);

printf('%d random numbers from 0 to %d are:n', n, max);
randomize();

for(c =1; c <= n; c++)
{
num = random(max);
printf('%dn',num);
}

getch();
return0;
}

In Unix-likeoperating systems, /dev/random, /dev/urandom and /dev/arandom are special files that serve as pseudorandom number generators. They allow access to environmental noise collected from device drivers and other sources.^[1]/dev/random typically blocks if there is less entropy available than requested; /dev/urandom typically never blocks, even if the pseudorandom number generator seed was not fully initialized with entropy since boot. /dev/arandom blocks after boot until the seed has been securely initialized with enough entropy, and then never blocks again. Not all operating systems implement the same methods for /dev/random and /dev/urandom and only a few provide /dev/arandom.

Example[edit]

This shell script is a random printable character generator, slightly biased:

Linux[edit]

Random number generation in kernel space was implemented for the first time for Linux^[2] in 1994 by Theodore Ts'o.^[3]The implementation used secure hashes rather than ciphers,^{[clarification needed]} to avoid cryptography export restrictions that were in place when the generator was originally designed. The implementation was also designed with the assumption that any given hash or cipher might eventually be found to be weak, and so the design is durable in the face of any such weaknesses. Fast recovery from pool compromise is not considered a requirement, because the requirements for pool compromise are sufficient for much easier and more direct attacks on unrelated parts of the operating system.

In Ts'o's implementation, the generator keeps an estimate of the number of bits of noise in the entropy pool. From this entropy pool random numbers are created. When read, the /dev/random device will only return random bytes within the estimated number of bits of noise in the entropy pool. When the entropy pool is empty, reads from /dev/random will block until additional environmental noise is gathered.^[4] The intent is to serve as a cryptographically secure pseudorandom number generator, delivering output with entropy as large as possible. This is suggested by the authors for use in generating cryptographic keys for high-value or long-term protection.^[4]

A counterpart to /dev/random is /dev/urandom ('unlimited'^[5]/non-blocking random source^[4]) which reuses the internal pool to produce more pseudo-random bits. This means that the call will not block, but the output may contain less entropy than the corresponding read from /dev/random. While /dev/urandom is still intended as a pseudorandom number generator suitable for most cryptographic purposes, the authors of the corresponding man page note that, theoretically, there may exist an as-yet-unpublished attack on the algorithm used by /dev/urandom, and that users concerned about such an attack should use /dev/random instead.^[4] However such an attack is unlikely to come into existence, because once the entropy pool is unpredictable it doesn't leak security by a reduced number of bits.^[6]

It is also possible to write to /dev/random. This allows any user to mix random data into the pool. Non-random data is harmless, because only a privileged user can issue the ioctl needed to increase the entropy estimate. The current amount of entropy and the size of the Linux kernel entropy pool, both measured in bits, are available in /proc/sys/kernel/random/ and can be displayed by the command cat /proc/sys/kernel/random/entropy_avail and cat /proc/sys/kernel/random/poolsize respectively.

Gutterman, Pinkas, & Reinman in March 2006 published a detailed cryptographic analysis of the Linux random number generator^[7] in which they describe several weaknesses. Perhaps the most severe issue they report is with embedded or Live CD systems, such as routers and diskless clients, for which the bootup state is predictable and the available supply of entropy from the environment may be limited. For a system with non-volatile memory, they recommend saving some state from the RNG at shutdown so that it can be included in the RNG state on the next reboot. In the case of a router for which network traffic represents the primary available source of entropy, they note that saving state across reboots 'would require potential attackers to either eavesdrop on all network traffic' from when the router is first put into service, or obtain direct access to the router's internal state. This issue, they note, is particularly critical in the case of a wireless router whose network traffic can be captured from a distance, and which may be using the RNG to generate keys for data encryption.

The Linux kernel provides support for several hardware random number generators, should they be installed. The raw output of such a device may be obtained from /dev/hwrng.^[8]

With Linux kernel 3.16 and newer,^[9] the kernel itself mixes data from hardware random number generators into /dev/random on a sliding scale based on the definable entropy estimation quality of the HWRNG. This means that no userspace daemon, such as rngd from rng-tools, is needed to do that job. With Linux kernel 3.17+, the VirtIO RNG was modified to have a default quality defined above 0,^[10] and as such, is currently the only HWRNG mixed into /dev/random by default.

The entropy pool can be improved by programs like timer_entropyd, haveged, randomsound etc. With rng-tools, hardware random number generators like Entropy Key, etc. can write to /dev/random. The diehard tests programs dieharder, diehard and ent can test these random number generators.^{[11][12][13][14]}

In January 2014, Daniel J. Bernstein published a critique^[15] of how Linux mixes different sources of entropy. He outlines an attack in which one source of entropy capable of monitoring the other sources of entropy could modify its output to nullify the randomness of the other sources of entropy. Consider the function H(x,y,z) where H is a hash function and x, y, and z are sources of entropy with z being the output of a CPU based malicious HRNG Z:

1. Z generates a random value of r.
2. Z computes H(x,y,r).
3. If the output of H(x,y,r) is equal to the desired value, output r as z.
4. Else, repeat starting at 1.

Bernstein estimated that an attacker would need to repeat H(x,y,r) 16 times to compromise DSA and ECDSA. This is possible because Linux reseeds H on an ongoing basis instead of using a single high quality seed.

In October 2016, with the release of Linux kernel version 4.8, the kernel's /dev/urandom was switched over to a ChaCha20-based cryptographic pseudorandom number generator (CPRNG) implementation^[16] by Theodore Ts'o, based on Bernstein's well-regarded stream cipherChaCha20.

In 2020, the Linux kernel version 5.6 /dev/random implementation was also switched to the ChaCha20-based CPRNG, which blocks only when the CPRNG hasn't initialized. Once initialized, /dev/random and /dev/urandom behave the same.^[17]

FreeBSD[edit]

The FreeBSD operating system provides /dev/urandom for compatibility but the behavior is very different from that of Linux. On FreeBSD, /dev/urandom is just a link to /dev/random and blocks only until properly seeded. FreeBSD's PRNG (Fortuna) reseeds regularly but does not attempt to estimate entropy. On a system with small amount of network and disk activity, reseeding is done after a fraction of a second.

While entropy pool–based methods are completely secure if implemented correctly, if they overestimate their entropy they may become less secure than well-seeded PRNGs. In some cases an attacker may have a considerable amount of control over the entropy; for example, a diskless server may get almost all of it from the network, rendering it potentially vulnerable to man-in-the-middle attacks.

OpenBSD[edit]

Since OpenBSD 5.1 (May 1, 2012) /dev/random and /dev/arandom use an algorithm based on RC4 but renamed, because of intellectual property reasons, ARC4. While random number generation here uses system entropy gathered in several ways, the ARC4 algorithm provides a fail-safe, ensuring that a rapid and high quality pseudo-random number stream is provided even when the pool is in a low entropy state. The system automatically uses hardware random number generators (such as those provided on some Intel PCI hubs) if they are available, through the OpenBSD Cryptographic Framework.

As of OpenBSD 5.5 (May 1, 2014), the arc4random() call used for OpenBSD's random devices no longer uses ARC4, but ChaCha20 (arc4random name might be reconsidered as A Replacement Call for Random).^[18][19]NetBSD's implementation of the legacy arc4random() API has also been switched over to ChaCha20 as well.^[20]

macOS and iOS[edit]

Darknives

macOS uses 160-bit Yarrow based on SHA-1.^[21] There is no difference between /dev/random and /dev/urandom; both behave identically.^[22]Apple's iOS also uses Yarrow.^[23]

Other operating systems[edit]

/dev/random and /dev/urandom are also available on Solaris,^[24]NetBSD,^[25]Tru64 UNIX 5.1B,^[26] AIX 5.2^[27] and HP-UX 11i v2.^[28] As with FreeBSD, AIX implements its own Yarrow-based design, however AIX uses considerably fewer entropy sources than the standard /dev/random implementation and stops refilling the pool when it thinks it contains enough entropy.^[29]

In Windows NT, similar functionality is delivered by ksecdd.sys, but reading the special file DeviceKsecDD does not work as in UNIX. The documented methods to generate cryptographically random bytes are CryptGenRandom and RtlGenRandom.

While DOS does not naturally provide such functionality, there is an open-source third-party driver called noise.sys,^[30] which functions similarly in that it creates two devices, RANDOM$ and URANDOM$, which are also accessible as /DEV/RANDOM$ and /DEV/URANDOM$, that programs can access for random data.

The Linux emulator Cygwin on Windows provide implementations of both /dev/random and /dev/urandom, which can be used in scripts and programs.^[31]

See also[edit]

• CryptGenRandom – The Microsoft Windows API's CSPRNG
• /dev

References[edit]

1. ^Torvalds, Linus (2005-04-16). 'Linux Kernel drivers/char/random.c comment documentation @ 1da177e4'. Retrieved 2014-07-22.
2. ^Lloyd, Jack (2008-12-09). 'On Syllable's /dev/random'. Retrieved 2019-08-21.
3. ^'/dev/random'. Everything2. 2003-06-08. Archived from the original on 2009-04-29. Retrieved 2013-07-03.
4. ^ ^abcdrandom(4) – Linux Programmer's Manual – Special Files
5. ^'/dev/random and /dev/urandom implementation in Linux 1.3.39, function random_read_unlimited'. 1995-11-04. Retrieved 2013-11-21.
6. ^Filippo Valsorda (2015-12-29). The plain simple reality of entropy.
7. ^Gutterman, Zvi; Pinkas, Benny; Reinman, Tzachy (2006-03-06). 'Analysis of the Linux Random Number Generator'(PDF). Archived(PDF) from the original on 2008-10-03. Retrieved 2013-07-03.
8. ^'Cryptography Users Guide'. Texas Instruments. 2013-06-04. Retrieved 2013-07-03.
9. ^'kernel/git/torvalds/linux.git - Linux kernel source tree @ be4000bc4644d027c519b6361f5ae3bbfc52c347 'hwrng: create filler thread''. Git.kernel.org. Retrieved 18 October 2016.
10. ^'kernel/git/torvalds/linux.git - Linux kernel source tree @ 34679ec7a0c45da8161507e1f2e1f72749dfd85c 'virtio: rng: add derating factor for use by hwrng core''. Git.kernel.org. Retrieved 18 October 2016.
11. ^'??'. Vanheusden.com. Archived from the original on 2013-09-21. Retrieved 2016-10-23.
12. ^'Google Code Archive for dieharder'. Code.google.com. Retrieved 18 October 2016.
13. ^'The Marsaglia Random Number CDROM including the Diehard Battery of Tests of Randomness'. Stat.fsu.edu. Archived from the original on 2016-01-25. Retrieved 2016-10-23.
14. ^'rng-tools'. Gnu.org. Retrieved 2016-10-23.
15. ^Daniel J. Bernstein (2014-02-05). 'cr.yp.to: 2014.02.05: Entropy Attacks!'.
16. ^'kernel/git/torvalds/linux.git - Linux kernel source tree'. kernel.org. 2016-07-27. Retrieved 2016-11-23.
17. ^'/dev/random Is More Like /dev/urandom With Linux 5.6 - Phoronix'. www.phoronix.com.
18. ^arc4random(3) – OpenBSD Library Functions Manual
19. ^deraadt, ed. (2014-07-21). 'libc/crypt/arc4random.c'. BSD Cross Reference, OpenBSD src/lib/. Retrieved 2015-01-13. ChaCha based random number generator for OpenBSD.
20. ^riastradh, ed. (2014-11-16). 'libc/gen/arc4random.c'. BSD Cross Reference, NetBSD src/lib/. Retrieved 2015-01-13. Legacy arc4random(3) API from OpenBSD reimplemented using the ChaCha20 PRF, with per-thread state.
21. ^'xnu-1456.1.26/bsd/dev/random'. Apple Inc. Retrieved 18 October 2016.
22. ^random(4) – Darwin and macOS Kernel Interfaces Manual
23. ^'iOS Security'(PDF). Apple Inc. October 2012. Archived from the original(PDF) on 2015-05-27. Retrieved 2015-05-27.
24. ^Moffat, Darren (2013-09-12). 'Solaris Random Number Generation'. Retrieved 2016-05-12.
25. ^rnd(4) – NetBSD Kernel Interfaces Manual
26. ^'random(4)'. 1999-09-19. Retrieved 2013-07-03.
27. ^'random and urandom Devices'. pSeries and AIX Information Center. 2010-03-15. Archived from the original on 2013-07-12. Retrieved 2013-07-03.
28. ^'HP-UX Strong Random Number Generator'. 2004-07-23. Retrieved 2013-07-03.
29. ^Roberts, Iain (2003-04-25). 'AIX 5.2 /dev/random and /dev/urandom devices'. Lists.gnupg.org. Archived from the original on 2012-02-22. Retrieved 2013-07-03.
30. ^'Doug Kaufman's Web Site - DOS ports'. 2006-11-02. Retrieved 2013-07-03.
31. ^'How does Cygwin's /dev/random and urandom work?'. www.linuxquestions.org. Retrieved 2018-03-09.

Dev Random Linux

External links[edit]

• Biege, Thomas (2006-11-06). 'Analysis of a strong Pseudo Random Number Generator by anatomizing Linux' Random Number Device'(PDF).
• Hühn, Thomas (2014). 'Myths about /dev/urandom'.

Dev Random C Code Download