KLEIN: A New Family of Lightweight Block Ciphers

Resource-efficient cryptographic primitives become fundamental for realizing both security and efficiency in embedded systems like RFID tags and sensor nodes. Among those primitives, lightweight block cipher plays a major role as a building block for security protocols. In this paper, we describe a new family of lightweight block ciphers named KLEIN, which is designed for resource-constrained devices such as wireless sensors and RFID tags. Compared to the related proposals, KLEIN has advantage in the software performance on legacy sensor platforms, while in the same time its hardware implementation can also be compact

Some Words on Cryptanalysis of Stream Ciphers

In the world of cryptography, stream ciphers are known as primitives used to ensure privacy over a communication channel. One common way to build a stream cipher is to use a keystream generator to produce a pseudo-random sequence of symbols. In such algorithms, the ciphertext is the sum of the keystream and the plaintext, resembling the one-time pad principal. Although the idea behind stream ciphers is simple, serious investigation of these primitives has started only in the late 20th century. Therefore, cryptanalysis and design of stream ciphers are important. In recent years, many designs of stream ciphers have been proposed in an effort to find a proper candidate to be chosen as a world standard for data encryption. That potential candidate should be proven good by time and by the results of cryptanalysis. Different methods of analysis, in fact, explain how a stream cipher should be constructed. Thus, techniques for cryptanalysis are also important. This thesis starts with an overview of cryptography in general, and introduces the reader to modern cryptography. Later, we focus on basic principles of design and analysis of stream ciphers. Since statistical methods are the most important cryptanalysis techniques, they will be described in detail. The practice of statistical methods reveals several bottlenecks when implementing various analysis algorithms. For example, a common property of a cipher to produce n-bit words instead of just bits makes it more natural to perform a multidimensional analysis of such a design. However, in practice, one often has to truncate the words simply because the tools needed for analysis are missing. We propose a set of algorithms and data structures for multidimensional cryptanalysis when distributions over a large probability space have to be constructed. This thesis also includes results of cryptanalysis for various cryptographic primitives, such as A5/1, Grain, SNOW 2.0, Scream, Dragon, VMPC, RC4, and RC4A. Most of these results were achieved with the help of intensive use of the proposed tools for cryptanalysis

A Cipher-Agnostic Neural Training Pipeline with Automated Finding of Good Input Differences

Neural cryptanalysis is the study of cryptographic primitives through machine learning techniques. Following Gohr’s seminal paper at CRYPTO 2019, a focus has been placed on improving the accuracy of such distinguishers against specific primitives, using dedicated training schemes, in order to obtain better key recovery attacks based on machine learning. These distinguishers are highly specialized and not trivially applicable to other primitives. In this paper, we focus on the opposite problem: building a generic pipeline for neural cryptanalysis. Our tool is composed of two parts. The first part is an evolutionary algorithm for the search of good input differences for neural distinguishers. The second part is DBitNet, a neural distinguisher architecture agnostic to the structure of the cipher. We show that this fully automated pipeline is competitive with a highly specialized approach, in particular for SPECK32, and SIMON32. We provide new neural distinguishers for several primitives (XTEA, LEA, HIGHT, SIMON128, SPECK128) and improve over the state-of-the-art for PRESENT, KATAN, TEA and GIMLI

Decorrelation: A Theory for Block Cipher Security

Pseudorandomness is a classical model for the security of block ciphers. In this paper we propose convenient tools in order to study it in connection with the Shannon Theory, the Carter-Wegman universal hash functions paradigm, and the Luby-Rackoff approach. This enables the construction of new ciphers with security proofs under specific models. We show how to ensure security against basic differential and linear cryptanalysis and even more general attacks. We propose practical construction scheme

Contributions to Confidentiality and Integrity Algorithms for 5G

The confidentiality and integrity algorithms in cellular networks protect the transmission of user and signaling data over the air between users and the network, e.g., the base stations. There are three standardised cryptographic suites for confidentiality and integrity protection in 4G, which are based on the AES, SNOW 3G, and ZUC primitives, respectively. These primitives are used for providing a 128-bit security level and are usually implemented in hardware, e.g., using IP (intellectual property) cores, thus can be quite efficient. When we come to 5G, the innovative network architecture and high-performance demands pose new challenges to security. For the confidentiality and integrity protection, there are some new requirements on the underlying cryptographic algorithms. Specifically, these algorithms should: 1) provide 256 bits of security to protect against attackers equipped with quantum computing capabilities; and 2) provide at least 20 Gbps (Gigabits per second) speed in pure software environments, which is the downlink peak data rate in 5G. The reason for considering software environments is that the encryption in 5G will likely be moved to the cloud and implemented in software. Therefore, it is crucial to investigate existing algorithms in 4G, checking if they can satisfy the 5G requirements in terms of security and speed, and possibly propose new dedicated algorithms targeting these goals. This is the motivation of this thesis, which focuses on the confidentiality and integrity algorithms for 5G. The results can be summarised as follows.1. We investigate the security of SNOW 3G under 256-bit keys and propose two linear attacks against it with complexities 2172 and 2177, respectively. These cryptanalysis results indicate that SNOW 3G cannot provide the full 256-bit security level. 2. We design some spectral tools for linear cryptanalysis and apply these tools to investigate the security of ZUC-256, the 256-bit version of ZUC. We propose a distinguishing attack against ZUC-256 with complexity 2236, which is 220 faster than exhaustive key search. 3. We design a new stream cipher called SNOW-V in response to the new requirements for 5G confidentiality and integrity protection, in terms of security and speed. SNOW-V can provide a 256-bit security level and achieve a speed as high as 58 Gbps in software based on our extensive evaluation. The cipher is currently under evaluation in ETSI SAGE (Security Algorithms Group of Experts) as a promising candidate for 5G confidentiality and integrity algorithms. 4. We perform deeper cryptanalysis of SNOW-V to ensure that two common cryptanalysis techniques, guess-and-determine attacks and linear cryptanalysis, do not apply to SNOW-V faster than exhaustive key search. 5. We introduce two minor modifications in SNOW-V and propose an extreme performance variant, called SNOW-Vi, in response to the feedback about SNOW-V that some use cases are not fully covered. SNOW-Vi covers more use cases, especially some platforms with less capabilities. The speeds in software are increased by 50% in average over SNOW-V and can be up to 92 Gbps.Besides these works on 5G confidentiality and integrity algorithms, the thesis is also devoted to local pseudorandom generators (PRGs). 6. We investigate the security of local PRGs and propose two attacks against some constructions instantiated on the P5 predicate. The attacks improve existing results with a large gap and narrow down the secure parameter regime. We also extend the attacks to other local PRGs instantiated on general XOR-AND and XOR-MAJ predicates and provide some insight in the choice of safe parameters

A survey on machine learning applied to symmetric cryptanalysis

In this work we give a short review of the recent progresses of machine learning techniques applied to cryptanalysis of symmetric ciphers, with particular focus on artificial neural networks. We start with some terminology and basics of neural networks, to then classify the recent works in two categories: "black-box cryptanalysis", techniques that not require previous information about the cipher, and "neuro-aided cryptanalysis", techniques used to improve existing methods in cryptanalysis

Secure execution environments through reconfigurable lightweight cryptographic components

Software protection is one of the most important problems in the area of computing as it affects a multitude of players like software vendors, digital content providers, users, and government agencies. There are multiple dimensions to this broad problem of software protection. The most important ones are: (1) protecting software from reverse engineering. (2) protecting software from tamper (or modification). (3) preventing software piracy. (4) verification of integrity of the software;In this thesis we focus on these areas of software protection. The basic requirement to achieve these goals is to provide a secure execution environment, which ensures that the programs behave in the same way as it was designed, and the execution platforms respect certain types of wishes specified by the program;We take the approach of providing secure execution environment through architecture support. We exploit the power of reconfigurable components in achieving this. The first problem we consider is to provide architecture support for obfuscation. This also achieves the goals of tamper resistance, copy protection, and IP protection indirectly. Our approach is based on the intuition that the software is a sequence of instructions (and data) and if the sequence as well the contents are obfuscated then all the required goals can be achieved;The second problem we solve is integrity verification of the software particularly in embedded devices. Our solution is based on the intuition that an obfuscated (permuted) binary image without any dynamic traces reveals very little information about the IP of the program. Moreover, if this obfuscation function becomes a shared secret between the verifier and the embedded device then verification can be performed in a trustworthy manner;Cryptographic components form the underlying building blocks/primitives of any secure execution environment. Our use of reconfigurable components to provide software protection in both Arc 3 D and TIVA led us to an interesting observation about the power of reconfigurable components. Reconfigurable components provide the ability to use the secret (or key) in a much stronger way than the conventional cryptographic designs. This opened up an opportunity for us to explore the use of reconfigurable gates to build cryptographic functions