Papers Cryptanalysis
“Cryptanalysis” 태그가 달린 논문 37편 · 필터 해제
Navigating the Deep: Signature Extraction on Deep Neural Networks
Neural network model extraction has emerged in recent years as an important security concern, as adversaries attempt to recover a network's parameters via black-box queries. A key step in this process is signature extrac…
CryptanalysisModel extractionBenchmarking Large Language Models for Cryptanalysis and Mismatched-Generalization
Recent advancements in Large Language Models (LLMs) have transformed natural language understanding and generation, leading to extensive benchmarking across diverse tasks. However, cryptanalysis a critical area for data …
BenchmarkingCryptanalysisNatural Language UnderstandingNeural-Inspired Advances in Integral Cryptanalysis
The study by Gohr et.al at CRYPTO 2019 and sunsequent related works have shown that neural networks can uncover previously unused features, offering novel insights into cryptanalysis. Motivated by these findings, we empl…
Computational EfficiencyCryptanalysisCryptanalysis via Machine Learning Based Information Theoretic Metrics
The fields of machine learning (ML) and cryptanalysis share an interestingly common objective of creating a function, based on a given set of inputs and outputs. However, the approaches and methods in doing so vary vastl…
CryptanalysisCryptanalysis of Cancelable Biometrics Vault
Cancelable Biometrics (CB) stands for a range of biometric transformation schemes combining biometrics with user specific tokens to generate secure templates. Required properties are the irreversibility, unlikability and…
CryptanalysisStructured Analysis and Comparison of Alphabets in Historical Handwritten Ciphers
Historical ciphered manuscripts are documents that were typically used in sensitive communications within military and diplomatic contexts or among members of secret societies. These secret messages were concealed by inv…
CryptanalysisOptical Character Recognition (OCR)Polynomial Time Cryptanalytic Extraction of Deep Neural Networks in the Hard-Label Setting
Deep neural networks (DNNs) are valuable assets, yet their public accessibility raises security concerns about parameter extraction by malicious actors. Recent work by Carlini et al. (crypto'20) and Canales-Mart\'inez et…
CryptanalysisTeaching Transformers Modular Arithmetic at Scale
Modular addition is, on its face, a simple operation: given $N$ elements in $\mathbb{Z}_q$, compute their sum modulo $q$. Yet, scalable machine learning solutions to this problem remain elusive: prior work trains ML mode…
CryptanalysisA Machine Learning-Based Framework for Assessing Cryptographic Indistinguishability of Lightweight Block Ciphers
Indistinguishability is a fundamental principle of cryptographic security, crucial for securing data transmitted between Internet of Things (IoT) devices. This principle ensures that an attacker cannot distinguish betwee…
CryptanalysisMemorizationTransfer LearningCryptanalysis of the SIMON Cypher Using Neo4j
The exponential growth in the number of Internet of Things (IoT) devices has seen the introduction of several Lightweight Encryption Algorithms (LEA). While LEAs are designed to enhance the integrity, privacy and securit…
CryptanalysisKnowledge GraphsCryptanalysis and improvement of multimodal data encryption by machine-learning-based system
With the rising popularity of the internet and the widespread use of networks and information systems via the cloud and data centers, the privacy and security of individuals and organizations have become extremely crucia…
CryptanalysisCan a Tabula Recta provide security in the XXI century?
In the not so unlikely scenario of total compromise of computers accessible to a group of users, they might be tempted to resort to human-computable paper-and-pencil cryptographic methods aided by a classic Tabula Recta,…
CryptanalysisLearning Quantum Processes with Quantum Statistical Queries
In this work, we initiate the study of learning quantum processes from quantum statistical queries. We focus on two fundamental learning tasks in this new access model: shadow tomography of quantum processes and process …
BenchmarkingCryptanalysisQuantum Machine LearningCRYPTO-MINE: Cryptanalysis via Mutual Information Neural Estimation
The use of Mutual Information (MI) as a measure to evaluate the efficiency of cryptosystems has an extensive history. However, estimating MI between unknown random variables in a high-dimensional space is challenging. Re…
CryptanalysisMemorization for Good: Encryption with Autoregressive Language Models
Over-parameterized neural language models (LMs) can memorize and recite long sequences of training data. While such memorization is normally associated with undesired properties such as overfitting and information leakin…
CryptanalysisMemorizationImproved Differential-neural Cryptanalysis for Round-reduced Simeck32/64
In CRYPTO 2019, Gohr presented differential-neural cryptanalysis by building the differential distinguisher with a neural network, achieving practical 11-, and 12-round key recovery attack for Speck32/64. Inspired by thi…
CryptanalysisUnclonability and Quantum Cryptanalysis: From Foundations to Applications
The impossibility of creating perfect identical copies of unknown quantum systems is a fundamental concept in quantum theory and one of the main non-classical properties of quantum information. This limitation imposed by…
CryptanalysisQuantum Machine LearningSALSA: 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 h…
CryptanalysisIdentifying Cleartext in Historical Ciphers
In historical encrypted sources we can find encrypted text sequences, also called ciphertext, as well as non-encrypted cleartexts written in a known language. While most of the cryptanalysis focuses on the decryption of …
CryptanalysisImproved (Related-key) Differential-based Neural Distinguishers for SIMON and SIMECK Block Ciphers
In CRYPTO 2019, Gohr made a pioneering attempt and successfully applied deep learning to the differential cryptanalysis against NSA block cipher SPECK32/64, achieving higher accuracy than the pure differential distinguis…
CryptanalysisDeep Learning