Biblio

Random numbers are essential for communications security, as they are widely employed as secret keys and other critical parameters of cryptographic algorithms. The Linux random number generator (LRNG) is the most popular open-source software-based random number generator (RNG). The security of LRNG is influenced by the overall design, especially the quality of entropy sources. Therefore, it is necessary to assess and quantify the quality of the entropy sources which contribute the main randomness to RNGs. In this paper, we perform an empirical study on the quality of entropy sources in LRNG with Linux kernel 5.6, and provide the following two findings. We first analyze two important entropy sources: jiffies and cycles, and propose a method to predict jiffies by cycles with high accuracy. The results indicate that, the jiffies can be correctly predicted thus contain almost no entropy in the condition of knowing cycles. The other important finding is the failure of interrupt cycles during system boot. The lower bits of cycles caused by interrupts contain little entropy, which is contrary to our traditional cognition that lower bits have more entropy. We believe these findings are of great significance to improve the efficiency and security of the RNG design on software platforms.

The development of edge computing and machine learning technologies have led to the growth of Industrial IoT systems. Autonomous decision making and smart manufacturing are flourishing in the current age of Industry 4.0. By providing more compute power to edge devices and connecting them to the internet, the so-called Cyber Physical Systems are prone to security threats like never before. Security in the current industry is based on cryptographic techniques that use pseudorandom number keys. Keys generated by a pseudo-random number generator pose a security threat as they can be predicted by a malicious third party. In this work, we propose a secure Industrial IoT Architecture that makes use of true random numbers generated by a quantum random number generator (QRNG). CITRIOT's FireConnect IoT node is used to show the proof of concept in a quantum-safe network where the random keys are generated by a cloud based quantum device. We provide an implementation of QRNG on both real quantum computer and quantum simulator. Then, we compare the results with pseudorandom numbers generated by a classical computer.

This paper introduces a robust random number generator that based on Bernoulli discrete chaotic map. An eight bit SAR ADC is used with discrete time chaotic map to generate random bit sequences. Compared to RNGs that use the continuous time chaotic map, sensitivity to process, voltage and temperature (PVT) variations are reduced. Thanks to utilizing switch capacitor circuits to implement Bernoulli chaotic map equations, power consumption decreased significantly. Proposed design that has a throughput of 500 Kbit/second is implemented in TSMC 180 nm process technology. Generated bit sequences has successfully passed all four primary tests of FIPS-140-2 test suite and all tests of NIST 820–22 test suite without post processing. Furthermore, data rate can be increased by sacrificing power consumption. Hence, proposed architecture could be utilized in high speed cryptography applications.

Random number generators play a pivotal role in generating security primitives, e.g., encryption keys, nonces, initial vectors, and random masking for side-channel countermeasures. A quantum entropy source based on radioactive isotope decay can be exploited to generate random numbers with sufficient entropy. If a deterministic random bit generator (DRBG) is combined for post-processing, throughput of the quantum random number generator (QRNG) can be improved. However, general DRBGs are susceptible to side-channel attacks. In this paper, we propose a framework called SCR-QRNG framework, which offers Side-Channel Resistant primitives using QRNG. The QRNG provides sources of randomness for modulating the clock frequency of a DRBG to obfuscate side-channel leakages, and to generate unbiased random numbers for security primitives. The QRNG has robustness against power side-channel attacks and is in compliance with NIST SP 800-22/90B and BSI AIS 31. We fabricate a quantum entropy chip, and implement a PCB module for a random frequency clock generator and a side-channel resistant QRNG on an FPGA.

Security down to hardware (HW) has become a fundamental requirement in highly-connected and ubiquitously deployed systems, as a result of the recent discovery of a wide range of vulnerabilities in commercial devices, as well as the affordability of several attacks that were traditionally considered unlikely. HW security is now a fundamental requirement in view of the massive attack surface that they expose, and the substantial power penalty entailed by solutions at higher levels of abstraction.In large-scale networks of connected devices, attacks need to be counteracted at low cost down to individual nodes, which need to be identified or authenticated securely, and protect confidentiality and integrity of the data that is sensed, stored, processed and wirelessly exchanged. In many security-sensitive applications, physical attacks against individual chips need to be counteracted to truly enable an end-to-end chain of trust from nodes to cloud and actuation (i.e., always-on security). These requirements have motivated the on-going global research and development effort to assure hardware security at low cost and power penalty down to low-end devices (i.e., ubiquitous security).This paper provides a fresh overview of the fundamentals, the design requirements and the state of the art in primitives for HW security. Challenges and future directions are discussed using recent silicon demonstrations as case studies.

Deep neural networks (DNNs) are effective machine learning models to solve a large class of recognition problems, including the classification of nonlinearly separable patterns. The applications of DNNs are, however, limited by the large size and high energy consumption of the networks. Recently, stochastic computation (SC) has been considered to implement DNNs to reduce the hardware cost. However, it requires a large number of random number generators (RNGs) that lower the energy efficiency of the network. To overcome these limitations, we propose the design of an energy-efficient deep belief network (DBN) based on stochastic computation. An approximate SC activation unit (A-SCAU) is designed to implement different types of activation functions in the neurons. The A-SCAU is immune to signal correlations, so the RNGs can be shared among all neurons in the same layer with no accuracy loss. The area and energy of the proposed design are 5.27% and 3.31% (or 26.55% and 29.89%) of a 32-bit floating-point (or an 8-bit fixed-point) implementation. It is shown that the proposed SC-DBN design achieves a higher classification accuracy compared to the fixed-point implementation. The accuracy is only lower by 0.12% than the floating-point design at a similar computation speed, but with a significantly lower energy consumption.

True random numbers have a fair role in modern digital transactions. In order to achieve secured authentication, true random numbers are generated as security keys which are highly unpredictable and non-repetitive. True random number generators are used mainly in the field of cryptography to generate random cryptographic keys for secure data transmission. The proposed work aims at the generation of true random numbers based on CMOS Boolean Chaotic Oscillator. As a part of this work, ASIC approach of CMOS Boolean Chaotic Oscillator is modelled and simulated using Cadence Virtuoso tool based on 45nm CMOS technology. Besides, prototype model has been implemented with circuit components and analysed using NI ELVIS platform. The strength of the generated random numbers was ensured by NIST (National Institute of Standards and Technology) Test Suite and ASIC approach was validated through various parameters by performing various analyses such as frequency, delay and power.

Random numbers represent one of the most sensible part of a cryptographic system, since the cryptographic keys must be entirely based on them. The security of a communication relies on the key that had been established between two users. If an attacker is able to deduce that key, the communication is compromised. This is why key generation must completely rely on random number generators, so that nobody can deduce the. This paper will describe a set of public and free Random Number Generators (RNG) within Android-based Smartphones by exploiting different sensors, along with the way of achieving this scope. Moreover, this paper will present some conclusive tests and results over them.