random — the entropy device
The random device returns an endless supply of random bytes when read. It also accepts and reads data as any ordinary file.
The generator will start in an unseeded state, and will block reads until it is seeded for the first time. This may cause trouble at system boot when keys and the like are generated from random so steps should be taken to ensure a seeding as soon as possible.
It is also possible to read random bytes by using the KERN_ARND sysctl. On the command line this could be done by
sysctl -x -B 16 kern.arandom
This sysctl will not return random bytes unless the random device is seeded.
This initial seeding of random number generators is a bootstrapping problem that needs very careful attention. In some cases, it may be difficult to find enough randomness to seed a random number generator until a system is fully operational, but the system requires random numbers to become fully operational. It is (or more accurately should be) critically important that the random device is seeded before the first time it is used. In the case where a dummy or "blocking-only" device is used, it is the responsibility of the system architect to ensure that no blocking reads hold up critical processes.
To see the current settings of the software random device, use the command line:
which results in something like:
kern.random.harvest.mask_symbolic: [HIGH_PERFORMANCE], ... ,CACHED
kern.random.random_sources: ’Intel Secure Key RNG’
all settings are read-only.
The kern.random.fortuna.minpoolsize sysctl is used to set the seed threshold. A smaller number gives a faster seed, but a less secure one. In practice, values between 64 and 256 are acceptable.
The kern.random.harvest.mask bitmask is used to select the possible entropy sources. A 0 (zero) value means the corresponding source is not considered as an entropy source. Set the bit to 1 (one) if you wish to use that source. The kern.random.harvest.mask_bin and kern.random.harvest.mask_symbolic sysctls can be used to confirm that the choices are correct. Note that disabled items in the latter item are listed in square brackets. See random_harvest(9) for more on the harvesting of entropy.
When options RANDOM_LOADABLE is used, the /dev/random device is not created until an "algorithm module" is loaded. Two of these modules are built by default, random_fortuna and random_yarrow. The random_yarrow module is deprecated, and will be removed in FreeBSD 12. Use of the Yarrow algorithm is not encouraged, but while still present in the kernel source, it can be selected with the options RANDOM_YARROW kernel option. Note that these loadable modules are slightly less efficient than their compiled-in equivalents. This is because some functions must be locked against load and unload events, and also must be indirect calls to allow for removal.
When options RANDOM_ENABLE_UMA is used, the /dev/random device will obtain entropy from the zone allocator. This is potentially very high rate, and if so will be of questionable use. If this is the case, use of this option is not recommended. Determining this is not trivial, so experimenting and measurement using tools such as dtrace(1) will be required.
The use of randomness in the field of computing is a rather subtle issue because randomness means different things to different people. Consider generating a password randomly, simulating a coin tossing experiment or choosing a random back-off period when a server does not respond. Each of these tasks requires random numbers, but the random numbers in each case have different requirements.
Generation of passwords, session keys and the like requires cryptographic randomness. A cryptographic random number generator should be designed so that its output is difficult to guess, even if a lot of auxiliary information is known (such as when it was seeded, subsequent or previous output, and so on). On FreeBSD, seeding for cryptographic random number generators is provided by the random device, which provides real randomness. The arc4random(3) library call provides a pseudo-random sequence which is generally reckoned to be suitable for simple cryptographic use. The OpenSSL library also provides functions for managing randomness via functions such as RAND_bytes(3) and RAND_add(3). Note that OpenSSL uses the random device for seeding automatically.
Randomness for simulation is required in engineering or scientific software and games. The first requirement of these applications is that the random numbers produced conform to some well-known, usually uniform, distribution. The sequence of numbers should also appear numerically uncorrelated, as simulation often assumes independence of its random inputs. Often it is desirable to reproduce the results of a simulation exactly, so that if the generator is seeded in the same way, it should produce the same results. A peripheral concern for simulation is the speed of a random number generator.
Another issue in simulation is the size of the state associated with the random number generator, and how frequently it repeats itself. For example, a program which shuffles a pack of cards should have 52! possible outputs, which requires the random number generator to have 52! starting states. This means the seed should have at least log_2(52!) ~ 226 bits of state if the program is to stand a chance of outputting all possible sequences, and the program needs some unbiased way of generating these bits. Again, the random device could be used for seeding here, but in practice, smaller seeds are usually considered acceptable.
FreeBSD provides two families of functions which are considered suitable for simulation. The random(3) family of functions provides a random integer between 0 to (2**31)−1. The functions srandom(3), initstate(3) and setstate(3) are provided for deterministically setting the state of the generator and the function srandomdev(3) is provided for setting the state via the random device. The drand48(3) family of functions are also provided, which provide random floating point numbers in various ranges.
Randomness that is used for collision avoidance (for example, in certain network protocols) has slightly different semantics again. It is usually expected that the numbers will be uniform, as this produces the lowest chances of collision. Here again, the seeding of the generator is very important, as it is required that different instances of the generator produce independent sequences. However, the guessability or reproducibility of the sequence is unimportant, unlike the previous cases.
FreeBSD does also provide the traditional rand(3) library call, for compatibility purposes. However, it is known to be poor for simulation and absolutely unsuitable for cryptographic purposes, so its use is discouraged.
Schneier , and
Cryptography Engineering ,
ISBN 978-0-470-47424-2 .
A random device appeared in FreeBSD 2.2. The current software implementation, introduced in FreeBSD 10.0, is by Mark R V Murray, and is an implementation of the Fortuna algorithm by Ferguson et al. It replaces the previous Yarrow implementation, introduced in FreeBSD 5.0. The Yarrow algorithm is no longer supported by its authors, and is therefore deprecated.
BSD August 17, 2015 BSD