New results on the genetic cryptanalysis of TEA and reduced-round versions of XTEA

Congress on Evolutionary Computation. Portland, USA, 19-23 June 2004Recently, a simple way of creating very efficient distinguishers for cryptographic primitives such as block ciphers or hash functions, was presented by the authors. Here, this cryptanalysis attack is shown to be successful when applied over reduced round versions of the block cipher XTEA. Additionally, a variant of this genetic attack is introduced and its results over TEA shown to be the most powerful published to date

The feature detection rule and its application within the negative selection algorithm

The negative selection algorithm developed by Forrest et al. was inspired by the manner in which T-cell lymphocytes mature within the thymus before being released into the blood system. The resultant T-cell lymphocytes, which are then released into the blood, exhibit an interesting characteristic: they are only activated by non-self cells that invade the human body. The work presented in this thesis examines the current body of research on the negative selection theory and introduces a new affinity threshold function, called the feature-detection rule. The feature-detection rule utilises the inter-relationship between both adjacent and non-adjacent features within a particular problem domain to determine if an artificial lymphocyte is activated by a particular antigen. The performance of the feature-detection rule is contrasted with traditional affinity-matching functions currently employed within negative selection theory, most notably the r-chunks rule (which subsumes the r-contiguous bits rule) and the hamming-distance rule. The performance will be characterised by considering the detection rate, false-alarm rate, degree of generalisation and degree of overfitting. The thesis will show that the feature-detection rule is superior to the r-chunks rule and the hamming-distance rule, in that the feature-detection rule requires a much smaller number of detectors to achieve greater detection rates and less false-alarm rates. The thesis additionally refutes that the way in which permutation masks are currently applied within negative selection theory is incorrect and counterproductive, while placing the feature-detection rule within the spectrum of affinity-matching functions currently employed by artificial immune-system (AIS) researchers.Dissertation (MSc)--University of Pretoria, 2009.Computer ScienceUnrestricte

Application of the feature-detection rule to the negative selection algorithm

The Negative Selection Algorithm developed by Forrest et al. was inspired by the way in which T-cell lymphocytes mature within the thymus before being released into the blood system. The mature T-cell lymphocytes exhibit an interesting characteristic, in that they are only activated by non-self cells that invade the human body. The Negative Selection Algorithm utilises an affinity matching function to ascertain whether the affinity between a newly generated (NSA) T-cell lymphocyte and a self-cell is less than a particular threshold; that is, whether the T-cell lymphocyte is activated by the self-cell. T-cell lymphocytes not activated by self-sells become mature T-cell lymphocytes. A new affinity matching function termed the feature-detection rule is introduced in this paper. The feature-detection rule utilises the interrelationship between both adjacent and non-adjacent features of a particular problem domain to determine whether an antigen is activated by an artificial lymphocyte. The performance of the featuredetection rule is contrasted with traditional affinity matching functions, currently employed within Negative Selection Algorithms, most notably the r-chunks rule (which subsumes the r-contiguous bits rule) and the hamming distance rule. This paper shows that the feature-detection rule greatly improves the detection rates and false alarm rates exhibited by the NSA (utilising the r-chunks and hamming distance rule) in addition to refuting the way in which permutation masks are currently being applied in artificial immune systems.http://www.elsevier.com/locate/esw

Side-Channel Attacks and Countermeasures for the MK-3 Authenticated Encryption Scheme

In the field of cryptography, the focus is often placed on security in a mathematical or information-theoretic sense; for example, cipher security is typically evaluated by the difficulty of deducing the plaintext from the ciphertext without knowledge of the key. However, once these cryptographic schemes are implemented in electronic devices, another class of attack presents itself. Side-channel attacks take advantage of the side effects of performing a computation, such as power consumption or electromagnetic emissions, to extract information outside of normal means. In particular, these side-channels can reveal parts of the internal state of a computation. This is important because intermediate values occurring during computation are typically considered implementation details, invisible to a potential attacker. If this information is revealed, then the assumptions of a non-side-channel-aware security analysis based only on inputs and outputs will no longer hold, potentially enabling an attack. This work tests the effectiveness of power-based side-channel attacks against MK-3, a customizable authenticated encryption scheme developed in a collaboration between RIT and L3Harris Technologies. Using an FPGA platform, Correlation Power Analysis (CPA) is performed on several different implementations of the algorithm to evaluate their resistance to power side-channel attacks. This method does not allow the key to be recovered directly; instead, an equivalent 512-bit intermediate state value is targeted. By applying two sequential stages of analysis, a total of between 216 and 322 bits are recovered, dependent on customization parameters. If a 128-bit key is used, then this technique has no benefit to an attacker over brute-forcing the key itself; however, in the case of a 256-bit key, CPA may provide up to a 66-bit advantage. In order to completely defend MK-3 against this type of attack, several potential countermeasures are discussed at the implementation, design, and overall system levels

Finding Efficient Distinguishers for Cryptographic Mappings, with an Application to the Block Cipher TEA

A simple way of creating new and very efficient distinguishers for cryptographic primitives, such as block ciphers or hash functions, is introduced. This technique is then successfully applied over reduced round versions of the block cipher TEA, which is proven to be weak with less than five cycles.This researchwas supported by project TIC2002-04498- C05-4 of the Spanish Ministerio de Ciencia y Tecnologia.Publicad

Pairing interacting protein sequences using masked language modeling

Predicting which proteins interact together from amino-acid sequences is an important task. We develop a method to pair interacting protein sequences which leverages the power of protein language models trained on multiple sequence alignments, such as MSA Transformer and the EvoFormer module of AlphaFold. We formulate the problem of pairing interacting partners among the paralogs of two protein families in a differentiable way. We introduce a method called DiffPALM that solves it by exploiting the ability of MSA Transformer to fill in masked amino acids in multiple sequence alignments using the surrounding context. MSA Transformer encodes coevolution between functionally or structurally coupled amino acids. We show that it captures inter-chain coevolution, while it was trained on single-chain data, which means that it can be used out-of-distribution. Relying on MSA Transformer without fine-tuning, DiffPALM outperforms existing coevolution-based pairing methods on difficult benchmarks of shallow multiple sequence alignments extracted from ubiquitous prokaryotic protein datasets. It also outperforms an alternative method based on a state-of-the-art protein language model trained on single sequences. Paired alignments of interacting protein sequences are a crucial ingredient of supervised deep learning methods to predict the three-dimensional structure of protein complexes. DiffPALM substantially improves the structure prediction of some eukaryotic protein complexes by AlphaFold-Multimer, without significantly deteriorating any of those we tested. It also achieves competitive performance with using orthology-based pairing.Comment: 33 pages, 14 figures, 2 table

Countermeasure implementation and effectiveness analysis for AES resistance against side channel attacks

Side Channel Analysis (SCA) is composed of a bunch of techniques employed to extract secret information from hardware operations through statistical analyses of execution data. For instance, the secret key of a crypto-algorithmic implementation could be targeted and its value could be retrieved. The data is obtained by measuring the power consumption or electromagnetic radiation of a device while performing an operation due to the linear relationship between the currents flowing through the circuitry during the execution of chip operations. Side channel is one of the most widely used attack methods in cryptanalysis. In order to avoid such attacks, the algorithmic implementations can be protected from side channel leakage with the use of different countermeasures. These countermeasures can be built on either software or hardware. The objective is to reduce, or even completely eliminate, the leakage of the device related to confidential data. Generally speaking, there are two main approaches to do so. The first aims to reduce the side channel observability, while the second intends to undermine the predictability of the data. This project focuses on designing and implementing different countermeasures that protect cryptographic implementations from side channel attacks, and test and analyze them afterwards. The countermeasures will be implemented in software and then tested though Correlation Power Analysis in a hardware device. The Advanced Encryption Standard (AES) algorithm will be used as a base structure, in order to improve its cryptographic security with the different countermeasures designed. However, the election of AES does not reduce the scope of this project since the implemented countermeasures could be applied to other cryptographic algorithms as well

Analysing Equilibrium States for Population Diversity

Population diversity is crucial in evolutionary algorithms as it helps with global exploration and facilitates the use of crossover. Despite many runtime analyses showing advantages of population diversity, we have no clear picture of how diversity evolves over time. We study how population diversity of $(\mu+1)$ algorithms, measured by the sum of pairwise Hamming distances, evolves in a fitness-neutral environment. We give an exact formula for the drift of population diversity and show that it is driven towards an equilibrium state. Moreover, we bound the expected time for getting close to the equilibrium state. We find that these dynamics, including the location of the equilibrium, are unaffected by surprisingly many algorithmic choices. All unbiased mutation operators with the same expected number of bit flips have the same effect on the expected diversity. Many crossover operators have no effect at all, including all binary unbiased, respectful operators. We review crossover operators from the literature and identify crossovers that are neutral towards the evolution of diversity and crossovers that are not.Comment: To appear at GECCO 202