Deep Learning-Based Cryptanalysis of Lightweight Block Ciphers

So, Jaewoo

doi:10.1155/2020/3701067

Cited by 44 publications

(48 citation statements)

References 30 publications

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“…Their findings showed that the trained models were able to generalize to ciphers that the models have not seen before. Last but not least, [30] attempted key recovery attacks on block ciphers, Simon and Speck, using deep learning. They were successful in recovering encryption keys for full-round Simon32/64 and Speck32/64 only when the keyspace was restricted to text-based keys.…”

Section: Background a Related Workmentioning

confidence: 99%

A Deep Learning Approach for Active S-Box Prediction of Lightweight Generalized Feistel Block Ciphers

et al. 2021

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One of the main security requirements for symmetric-key block ciphers is resistance against differential cryptanalysis. This is commonly assessed by counting the number of active substitution boxes (S-boxes) using search algorithms or mathematical solvers that incur high computational costs. These costs increase exponentially with respect to block cipher size and rounds, quickly becoming inhibitive. Conventional S-box enumeration methods also require niche cryptographic knowledge to perform. In this paper, we overcome these problems by proposing a data-driven approach using deep neural networks to predict the number of active S-boxes. Our approach trades off exactness for real-time efficiency as the bulk of computational work is brought over to pre-processing (training). Active S-box prediction is framed as a regression task whereby neural networks are trained using features such as input and output differences, number of rounds, and permutation pattern. We first investigate the feasibility of the proposed approach by applying it on a reduced (4-branch) generalized Feistel structure (GFS) cipher. Apart from optimizing a neural network architecture for the task, we also explore the impact of each feature and its representation on prediction error. We then extend the idea to 64-bit GFS ciphers by first training neural networks using data from five different ciphers before using them to predict the number of active S-boxes for TWINE, a lightweight block cipher. The best performing model achieved the lowest root mean square error of 1.62 and R 2 of 0.87, depicting the feasibility of the proposed approach.INDEX TERMS Active S-boxes, block cipher, cryptanalysis, deep learning, differential cryptanalysis, lightweight cryptography, neural networks, TWINE

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Section: Background a Related Workmentioning

confidence: 99%

A Deep Learning Approach for Active S-Box Prediction of Lightweight Generalized Feistel Block Ciphers

et al. 2021

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“…e goal of this paper is to develop a novel machine learning-based protocol analysis scheme with much better efficiency that can discover more security attacks and vulnerabilities. Previously, the application of machine learning in security analysis has been mainly limited to side-channel attack [10,11] and symmetric cryptoanalysis [12,13]. Our motivation for applying machine learning in protocol analysis is described as follows:…”

Section: Motivation and Goal Of Is Papermentioning

confidence: 99%

A Novel Machine Learning-Based Approach for Security Analysis of Authentication and Key Agreement Protocols

Zahednejad

2020

Security and Communication Networks

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The application of machine learning in the security analysis of authentication and key agreement protocol was first launched by Ma et al. in 2018. Although they received remarkable results with an accuracy of 72% for the first time, their analysis is limited to replay attack and key confirmation attack. In addition, their suggested framework is based on a multiclassification problem in which every protocol or dataset instance is either secure or prone to a security attack such as replay attack, key confirmation, or other attacks. In this paper, we show that multiclassification is not an appropriate framework for such analysis, since authentication protocols may suffer different attacks simultaneously. Furthermore, we consider more security properties and attacks to analyze protocols against. These properties include strong authentication and Unknown Key Share (UKS) attack, key freshness, key authentication, and password guessing attack. In addition, we propose a much more efficient dataset construction model using a tenth number of features, which improves the solving speed to a large extent. The results indicate that our proposed model outperforms the previous models by at least 10–20 percent in all of the machine learning solving algorithms such that upper-bound performance reaches an accuracy of over 80% in the analysis of all security properties and attacks. Despite the previous models, the classification accuracy of our proposed dataset construction model rises in a rational manner along with the increase of the dataset size.

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“…Roughly speaking, cryptanalysis aims to test and analyze the security of cryptographic protocols by feeding different inputs to the cryptographic algorithm and analyzing the outputs in order to find a common or repetitive pattern in the outputs that might help find the secret key or even decrypt the ciphertext without access to the key. Machine learning can help learn from the data generated by the cryptographic algorithm and detect significant patterns [6]- [8] In late 90's and early 2000's, several cryptographic protocols using machine learning and deep learning models were proposed such as [9]- [11], but were deemed insecure and even some concrete attacks [12] were shown subsequently. The interest in neural network based cryptography took a dip because of the fact that simple computations, even as basic as exclusive-or (XOR) operation could not be computed by simple neural networks.…”

Section: Introductionmentioning

confidence: 99%

Neural Networks-Based Cryptography: A Survey

Meraouche¹,

Dutta

Tan

et al. 2021

IEEE Access

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A current trend of research focuses on artificial intelligence based cryptography which although proposed almost thirty years ago could not attract much attention. Abadi and Anderson's work on adversarial cryptography in 2016 rejuvenated the research area which now focuses in building neural networks that are able to learn cryptography using the idea from Generative Adversarial Networks (GANs). In this paper, we survey the most prominent research works that cover neural networks based cryptography from two main periods. The first period covers the oldest models that have been proposed shortly after 2000 and the second period covers the more recent models that have been proposed since 2016. We first discuss the implementation of the systems from the earlier era and the attacks mounted on them. After that, we focus on post 2016 era where more advanced techniques are utilized that rely on GANs in which neural networks compete with each other in order to achieve a goal e.g. learning to encrypt a communication. Finally, we discuss security analysis performed on adversarial cryptography models.INDEX TERMS cryptography, deep learning, neural networks, generative adversarial networks

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Deep Learning-Based Cryptanalysis of Lightweight Block Ciphers

Cited by 44 publications

References 30 publications

A Deep Learning Approach for Active S-Box Prediction of Lightweight Generalized Feistel Block Ciphers

A Deep Learning Approach for Active S-Box Prediction of Lightweight Generalized Feistel Block Ciphers

A Novel Machine Learning-Based Approach for Security Analysis of Authentication and Key Agreement Protocols

Neural Networks-Based Cryptography: A Survey

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