1,185 research outputs found
Letter counting: a stem cell for Cryptology, Quantitative Linguistics, and Statistics
Counting letters in written texts is a very ancient practice. It has
accompanied the development of Cryptology, Quantitative Linguistics, and
Statistics. In Cryptology, counting frequencies of the different characters in
an encrypted message is the basis of the so called frequency analysis method.
In Quantitative Linguistics, the proportion of vowels to consonants in
different languages was studied long before authorship attribution. In
Statistics, the alternation vowel-consonants was the only example that Markov
ever gave of his theory of chained events. A short history of letter counting
is presented. The three domains, Cryptology, Quantitative Linguistics, and
Statistics, are then examined, focusing on the interactions with the other two
fields through letter counting. As a conclusion, the eclectism of past
centuries scholars, their background in humanities, and their familiarity with
cryptograms, are identified as contributing factors to the mutual enrichment
process which is described here
Mind the Gap - A Closer Look at the Security of Block Ciphers against Differential Cryptanalysis
Resistance against differential cryptanalysis is an important design criteria for any modern block cipher and most designs rely on finding some upper bound on probability of single differential characteristics. However, already at EUROCRYPT'91, Lai et al. comprehended that differential cryptanalysis rather uses differentials instead of single characteristics.
In this paper, we consider exactly the gap between these two approaches and investigate this gap in the context of recent lightweight cryptographic primitives. This shows that for many recent designs like Midori, Skinny or Sparx one has to be careful as bounds from counting the number of active S-boxes only give an inaccurate evaluation of the best differential distinguishers. For several designs we found new differential distinguishers and show how this gap evolves. We found an 8-round differential distinguisher for Skinny-64 with a probability of 2â56.932â56.93, while the best single characteristic only suggests a probability of 2â722â72. Our approach is integrated into publicly available tools and can easily be used when developing new cryptographic primitives.
Moreover, as differential cryptanalysis is critically dependent on the distribution over the keys for the probability of differentials, we provide experiments for some of these new differentials found, in order to confirm that our estimates for the probability are correct. While for Skinny-64 the distribution over the keys follows a Poisson distribution, as one would expect, we noticed that Speck-64 follows a bimodal distribution, and the distribution of Midori-64 suggests a large class of weak keys
A forensics software toolkit for DNA steganalysis.
Recent advances in genetic engineering have allowed the insertion of artificial DNA strands into the living cells of organisms. Several methods have been developed to insert information into a DNA sequence for the purpose of data storage, watermarking, or communication of secret messages. The ability to detect, extract, and decode messages from DNA is important for forensic data collection and for data security. We have developed a software toolkit that is able to detect the presence of a hidden message within a DNA sequence, extract that message, and then decode it. The toolkit is able to detect, extract, and decode messages that have been encoded with a variety of different coding schemes. The goal of this project is to enable our software toolkit to determine with which coding scheme a message has been encoded in DNA and then to decode it. The software package is able to decode messages that have been encoded with every variation of most of the coding schemes described in this document. The software toolkit has two different options for decoding that can be selected by the user. The first is a frequency analysis approach that is very commonly used in cryptanalysis. This approach is very fast, but is unable to decode messages shorter than 200 words accurately. The second option is using a Genetic Algorithm (GA) in combination with a Wisdom of Artificial Crowds (WoAC) technique. This approach is very time consuming, but can decode shorter messages with much higher accuracy
Automatic Generation of Declarative Models For Differential Cryptanalysis
When designing a new symmetric block cipher, it is necessary to evaluate its robustness against differential attacks. This is done by computing Truncated Differential Characteristics (TDCs) that provide bounds on the complexity of these attacks. TDCs are often computed by using declarative approaches such as CP (Constraint Programming), SAT, or ILP (Integer Linear Programming). However, designing accurate and efficient models for these solvers is a difficult, error-prone and time-consuming task, and it requires advanced skills on both symmetric cryptography and solvers.
In this paper, we describe a tool for automatically generating these models, called Tagada (Tool for Automatic Generation of Abstraction-based Differential Attacks). The input of Tagada is an operational description of the cipher by means of black-box operators and bipartite Directed Acyclic Graphs (DAGs). Given this description, we show how to automatically generate constraints that model operator semantics, and how to generate MiniZinc models. We experimentally evaluate our approach on two different kinds of differential attacks (e.g., single-key and related-key) and four different symmetric block ciphers (e.g., the AES (Advanced Encryption Standard), Craft, Midori, and Skinny). We show that our automatically generated models are competitive with state-of-the-art approaches. These automatically generated models constitute a new benchmark composed of eight optimization problems and eight enumeration problems, with instances of increasing size in each problem. We experimentally compare CP, SAT, and ILP solvers on this new benchmark
SALSA: Attacking Lattice Cryptography with Transformers
Currently deployed public-key cryptosystems will be vulnerable to attacks by
full-scale quantum computers. Consequently, "quantum resistant" cryptosystems
are in high demand, and lattice-based cryptosystems, based on a hard problem
known as Learning With Errors (LWE), have emerged as strong contenders for
standardization. In this work, we train transformers to perform modular
arithmetic and combine half-trained models with statistical cryptanalysis
techniques to propose SALSA: a machine learning attack on LWE-based
cryptographic schemes. SALSA can fully recover secrets for small-to-mid size
LWE instances with sparse binary secrets, and may scale to attack real-world
LWE-based cryptosystems.Comment: Extended version of work published at NeurIPS 202
Cryptanalysis of ITRU
ITRU cryptosystem is a public key cryptosystem and one of the known variants of NTRU cryptosystem. Instead of working in a truncated polynomial ring, ITRU cryptosystem is based on the ring of integers. The authors claimed that ITRU has better features comparing to the classical NTRU, such as having a simple parameter selection algorithm, invertibility, and successful message decryption, and better security. In this paper, we present an attack technique against the ITRU cryptosystem, and it is mainly based on a simple frequency analysis on the letters of ciphertexts
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