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International Conference on Telecommunication in Modern Satellite, Cable and Broadcasting Services

True random binary sequence generator for secure communications

Slavko Šajić, Branislav M. Todorović, Nebojša Maletić

Published Oct 1, 2011• 2 citations
Publisher

Abstract

The true random binary sequence generator provides a secure, non-reproducible source of true random binary sequences for applications with strong security requirements, such as for generating encryption keys and for secure communications. In this paper we present a realization of true random binary sequence generator. Thermal noise found in chaotic physical system is used as a source of true randomness. NIST tests for verification of randomness are applied. Some experimental results are presented.

DOI
10.1109/telsks.2011.6143213

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The true random binary sequence generator provides a secure, non-reproducible source of true random binary sequences for applications with strong security requirements, such as for generating encryption keys and for secure communications. In this paper we present a realization of true random binary sequence generator. Thermal noise found in chaotic physical system is used as a source of true randomness. NIST tests for verification of randomness are applied. Some experimental results are presented.
One key requirement for many cryptograhic schemes is the generation of random numbers.Sequences of random numbers are used at several stages of a standard cryptographic protocol.One simple example is a Vernam cipher, where a string of random numbers is added to message string to generate encrypted code.C = M ⊕ K.It has been mathematically shown that this simple scheme is unbreakable if key K is as long as M and is used only once.The security of a cryptosystem shall not be based on keeping the algorithm secret but solely on keeping the key secret.The security of a random number generator (RNG) is related to the difficulty of predicting its future sequence values from past values.The quality and unpredictability of secret data is critical to securing communication by modern cryptographic techniques.The generation of such data for cryptographic purposes typically requires an unpredictable physical source of random data.We studied a chaotic circuit which consisted of an inductor, capacitance, diode and thus used for the BB84 protocol.We have studied both pseudo random and true random number generators and evaluated them through various tests like frequency, correlation, NIST etc.
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Nowadays information technologies are widespread and used in every computer-based system, hence information security tasks are quite important and their successful solution is required in business process. Cryptographic means are used in different applications, especially in cases when data confidentiality should be provided; although they can be utilized to maintain data availability and integrity, user's anonymity, author's non-repudiation and so on. Many information security tools use random numbers, but unfortunately, quality of output random numbers and speed of their generation do not satisfy modern requirements. The rate of production-run random number generators is limited by the physical processes used. One of the reasons of low random numbers generating rate is application of binary events. Many generators use analog events, e.g. noise in electronic devices, converted to binary numbers utilizing the threshold value or quantum discrete events, e.g. photon passing through the polarizer. The main idea of this work is that one can increase random number generator's rate using the non-binary sequences, e.g. non-binary quantum processes.
True random bits are crucial for cryptographic systems, particularly in key generation and secure initialization. In this paper, we present a novel technique based on chaotic signals and the addition of pulse-amplitude modulation signals to generate random bits. This technique can be implemented directly in physical systems. The randomness of our generated bit sequences will be examined with the National Institute of Standards and Technology (NIST) statistical test. The results will also be compared with a widely used technique reported in the literature.
Generating random bit streams is required in various applications, most notably in cyber-security, which is essential for Internet of Everything applications to enable secure communication between interconnected devices. Ensuring high-quality and robust randomness is crucial to mitigate risks associated with predictability and system compromise. True random numbers provide the highest levels of unpredictability. However, known systematic biases that can emerge from physical imperfections, environmental variations, and device aging in the processes exploited for random number generation must be carefully monitored. This article reports the implementation and characterization of an online procedure for the detection of anomalies in a true random bit stream. It is based on the NIST adaptive proportion and repetition count tests, complemented by statistical analysis relying on the Monobit and RUNS tests. The procedure is implemented in firmware through dedicated hardware accelerators processing configurable-length sequences, with automated anomaly detection triggering alerts after three consecutive threshold violations. The implementation is performed simultaneously with bit stream generation and also provides an estimate of the entropy of the source. A statistical analysis of the results from the NIST procedure to evaluate the symbols of the bit-stream as independently and identically distributed is also performed, leading to a computation of the minimum entropy of the source that cross-checks the previously mentioned estimate. The experimental validation of the approach is performed up the bit streams generated by a quantum, silicon-based entropy source.
Random numbers play a central role in cryptography, cyber security, and gaming applications. Extracting bits from some random natural activity is one of the primitive ways to generate random numbers. True random number generator(TRNG) and quantum random number generator(QRNG) are examples of this random number sequence. The development of quantum technology made it possible to extract random sequences from quantum entropy sources. This sequence is called quantum random numbers and the whole setup is named a quantum random number generator. A suitable sampling and a physical device for the entropy source are the building blocks for a TRNG. Here we present a comparative analysis of two random number generators. Apart from the conventional statistical test for randomness, here we introduce a straightforward coefficient based on the concept of non-linearity of the Boolean function to determine the availability of any linearity in a chosen random binary sequence. It reveals the cryptographic suitability of the generated sequence w.r.t any correlation attack. A comparison of the nonlinearity of two Boolean sequences generated from two different random sources is presented.