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International Scientific-Practical Conference Problems of Infocommunications Science and Technology

Differential electromagnetic attack on cryptographies modules of a quantum random number generator

Yuriy Gorbenko, Олексій Нарєжній, Maxim Krivich

Published Oct 1, 2017• 4 citations
Publisher

Abstract

This paper describes a differential electromagnetic analysis (DEMA) attack that is performed on a software implementation of the AES-128 in cryptographic modulo (extractor) of quantum random number generator (QRNG). A DEMA attack on an improved extractor implementation demonstrates that for correlation analysis it is required series of measurements to find the key bits. The metric of attack success is determined by the number of measurements needed to intercept the round keys. An efficient way to perform the correlation between theoretical predictions and real measurements it is the usage of the "F-tests".

DOI
10.1109/infocommst.2017.8246372

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This paper describes a differential electromagnetic analysis (DEMA) attack that is performed on a software implementation of the AES-128 in cryptographic modulo (extractor) of quantum random number generator (QRNG). A DEMA attack on an improved extractor implementation demonstrates that for correlation analysis it is required series of measurements to find the key bits. The metric of attack success is determined by the number of measurements needed to intercept the round keys. An efficient way to perform the correlation between theoretical predictions and real measurements it is the usage of the "F-tests".
The paper presents the results of applying an advanced attack on an external channel to an extractor of a quantum random number generator (QRNG). The attack is based on the verification of mathematical models of the interception channel and the EM signal of the extractor. Numerical simulation of the electromagnetic (EM) signal and the master key interception channel was performed based on the implementation of the DEMA algorithm of the attack on the AES-128 cipher. Measurements of the parameters of the EM signal and the interception channel were carried out on an experimental setup using a modern reference base in the field of electromagnetic compatibility.
Topicality. Reliable random number generation is one of the key requirements for modern cryptographic systems and cybersecurity applications. Traditional pseudo-random number generators are based on deterministic algorithms and may exhibit limited entropy and predictability, which can negatively affect information security. The subject of study in the article is methods of quantum random number generation and approaches to evaluating the quality of generated random sequences. The purpose of the article is to analyze modern methods of quantum random number generation, develop a software tool for obtaining quantum random numbers based on the ANU Quantum Random Number Generator service, perform statistical testing of the generated sequences, and analyze the obtained results. The following results were obtained. Modern quantum random number generation systems were analyzed and compared. A software tool was implemented in Python for automatic retrieval of quantum random numbers through the ANU QRNG API. The generated binary sequences were evaluated using Frequency Test and Block Frequency Test from the NIST SP 800-22 statistical test suite. The obtained p-values were 0.977181 and 0.200659 respectively, confirming successful completion of both tests. Additionally, visual analysis of the generated sequences revealed no noticeable patterns or repetitive structures. Conclusion. The obtained results confirm the high quality of quantum random sequences generated using ANU QRNG and demonstrate the applicability of the developed software tool for cryptographic and cybersecurity applications requiring high-quality randomness.
Random number generators (RNGs) that are crucial for cryptographic applications have been the subject of adversarial attacks. These attacks exploit environmental information to predict generated random numbers that are supposed to be truly random and unpredictable. Though quantum random number generators (QRNGs) are based on the intrinsic indeterministic nature of quantum properties, the presence of classical noise in the measurement process compromises the integrity of a QRNG. In this paper, we develop a predictive machine learning (ML) analysis to investigate the impact of deterministic classical noise in different stages of an optical continuous variable QRNG. Our ML model successfully detects inherent correlations when the deterministic noise sources are prominent. After appropriate filtering and randomness extraction processes are introduced, our QRNG system, in turn, demonstrates its robustness against ML. We further demonstrate the robustness of our ML approach by applying it to uniformly distributed random numbers from the QRNG and a congruential RNG. Hence, our result shows that ML has potentials in benchmarking the quality of RNG devices.
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.
Random prime number generation is crucial for the implementation of several encryption and signature protocols in cryptographic applications. Recently, Quantum Random Number Generators (QRNGs) have emerged as a solution to obtain information-theoretically secure randomness, setting them apart from algorithmic generators. Nonetheless, their output is still prone to deterioration under a flawed implementation and thus requires continuous statistical validation. This process is uniquely challenging for prime number generators since they cannot be submitted to the traditional testing suites, which are primarily designed to test uniform pseudorandom sources. Here, we enable direct statistical testing of prime Random Number Generators (RNGs) by extending a validation framework based on an equiprobable binning of the prime distribution with a predictive machine learning model, which can independently learn correlations in the prime output. The validity of this framework is then verified through extensive cryptanalysis of a prime QRNG based on quadrature measurements of the vacuum state. The developed model successfully identifies flawed configurations of the prime QRNG for output lengths up to 128 bits. This assessment was additionally extended to a purely classical scheme derived from electronic noise measurements. Although no increase in the model’s predictive capacity is seen, our testing framework can nonetheless demonstrate that this classical source produces a conclusively biased prime distribution. In turn, the implemented QRNG remains resilient against this type of cryptanalysis.
Random numbers are of vital importance in today’s world and used for example in many cryptographical protocols to secure the communication over the internet. The generators producing these numbers are Pseudo Random Number Generators (PRNGs) or True Random Number Generators (TRNGs). A subclass of TRNGs are the Quantum based Random Number Generators (QRNGs) whose generation processes are based on quantum phenomena. However, the achievable quality of the numbers generated from a practical implementation can differ from the theoretically possible. To ease this negative effect post-processing can be used, which contains the use of extractors. They extract as much entropy as possible from the original source and produce a new output with better properties. The quality and the different properties of a given output can be measured with the help of statistical tests. In our work we examined the effect of different extractors on two QRNG outputs and found that witg the right extractor we can improve their quality.