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A USB-pluggable hardware true random number generator. In computing, a hardware random number generator (HRNG), true random number generator (TRNG), non-deterministic random bit generator (NRBG), [1] or physical random number generator [2] [3] is a device that generates random numbers from a physical process capable of producing entropy (in other words, the device always has access to a ...
Some hardware devices also include registers that are not visible to software, for their internal use. Depending on their complexity, modern hardware devices can have many registers. Standard integrated circuits typically document their externally-exposed registers as part of their electronic component datasheet .
RDRAND (for "read random") is an instruction for returning random numbers from an Intel on-chip hardware random number generator which has been seeded by an on-chip entropy source. [1] It is also known as Intel Secure Key Technology , [ 2 ] codenamed Bull Mountain . [ 3 ]
MAC addresses are primarily assigned by device manufacturers, and are therefore often referred to as the burned-in address, or as an Ethernet hardware address, hardware address, or physical address. Each address can be stored in the interface hardware, such as its read-only memory , or by a firmware mechanism.
Lavarand, also known as the Wall of Entropy, is a hardware random number generator designed by Silicon Graphics that worked by taking pictures of the patterns made by the floating material in lava lamps, extracting random data from the pictures, and using the result to seed a pseudorandom number generator.
This message may contain the client's Client ID (Option 61, containing a unique value, traditionally a MAC address), the IP address that the server is offering, the subnet mask, the lease duration, and the IP address of the DHCP server making the offer. The DHCP server may also take notice of the hardware-level MAC address (as specified in the ...
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A few cryptographically secure pseudorandom number generators do not rely on cipher algorithms but try to link mathematically the difficulty of distinguishing their output from a `true' random stream to a computationally difficult problem. These approaches are theoretically important but are too slow to be practical in most applications.