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Content | Automotive Compilation Vol. 8 30 Random Number Generation Using AES Toby Prescott Use of Random Numbers in Security Protocols Random number generation has applications in many areas, but this article will focus on the use of these numbers in security protocols. Specifically, the security protocols used to protect the opening and starting of an automobile will be examined. While equally important, much of the focus has been on the choice and implementation of the cryptographic engine that protects the exchange of information between the vehicle and key fob used to establish the authenticity of the owner. Many of these security protocols use a random number as a way to ensure freshness of the communication. These random numbers are vastly important for achieving a high level of security, and in many cases are incorrectly assumed to be easily obtained. There are numerous examples of security protocols being compromised not by a direct attack on the encryption itself but instead by focusing on removing the unpredictability from the random number generation process. [Mifare.pdf] This article attempts to bring this critical part of the security system into full focus and present an effective method for generating random numbers. Random Number Generation Background Random numbers have been generated in many ways in order to introduce a needed element of unpredictability. This can range from being as simple as flipping a coin to more complicated procedures such as measuring the decay of radioactive elements. The desired result is that it should be impossible to predict with any accuracy the next outcome based on past or present knowledge. Much of the application of random numbers to security systems is based on Claude E Shannon’s work with information theory. A general definition of a random number is that when drawn from a given set of numbers, the probability of any one number should be equal to that of all others, in order for it to be truly random. The process of generating a random number will typically fall into two distinct categories: True Random Number Generators (TRNG) and Pseudo-Random Number Generators (PRNG). Typically, the TRNG is tied to some physical noise source, ranging from a noisy diode to quantum noise. PRNG results from the need to produce a random number in an application where a TRNG is not practical to implement. The PRNG attempts to achieve a result sufficiently indistinguishable from TRNG for the application using the random number. Examples of this would be measuring the movement of a computer mouse over time or sampling a high-speed counter each time a button is pressed by the user. While it is technically possible to manipulate or control these in such a way as to produce numbers that are not random, the feasibility of implementing the attack ensures that these methods of PRNG are sufficient for low- risk applications. This article focuses on a distinct subset of PRNG known as Deterministic Random Bit Generators (DRBG). These are unique in that they are based on the use of algorithms that are very predictable by nature. While it is possible to generate good-quality random numbers using these methods, there are some specific conditions that must be carefully considered. The recommendations in this article are based on the National Institute of Standards and Technology (NIST) publication SP800-90. |

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