Recent Trends in Cryptanalysis Techniques: A Review

Sikdar, Sourav; Kule, Malay

doi:10.1007/978-981-99-2680-0_19

Cited by 1 publication

(2 citation statements)

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“…Among these, machine learning algorithms are mainly used to optimize brute-force searches or assist in analyzing side-channel information effectively. Even using machine learning approaches, successfully breaking AES encryption through cryptanalysis remains a substantial issue [15], as its robust design ideas, mathematical foundations, and resistance to known attacks are largely responsible for its security.…”

Section: ) Advanced Encryption Standard (Aes)mentioning

confidence: 99%

“…Deep learning has shown remarkable success in various domains, from image recognition to Natural Language Processing (NLP) [14]. It is no surprise that deep learning is increasingly used in research involving cryptography [3] and cryptanalysis [15]. The proposed framework employs state-ofthe-art deep learning techniques, such as Convolution Neural Networks (CNNs) [16], Gated Recurrent Units (GRUs) [17], and Long Short-Term Memory (LSTM) [18] to process sequential data.…”

Section: Introductionmentioning

confidence: 99%

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Deep-Learning-based Cryptanalysis through Topic Modeling

Kumar,

Tanwar,

Kumar

2024

Eng. Technol. Appl. Sci. Res.

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Neural cryptography is a technique that uses neural networks for secure data encryption. Cryptoanalysis, on the other hand, deals with analyzing and decrypting ciphers, codes, and encrypted text without using a real key. Chosen-plaintext cryptanalysis is a subfield of cryptanalysis where both plain text and ciphertext are available and the goal is either to find the encryption technique, the encryption key, or both. This study addresses chosen plaintext cryptanalysis within public key cryptography, to categorize topics of encrypted text. Using a fixed encryption technique and key, the focus was placed on creating a framework that identifies the topic associated with ciphertext, using diverse plaintexts and their corresponding cipher texts. To our knowledge, this is the first time that chosen-plaintext cryptanalysis has been discussed in the context of topic modeling. The paper used deep learning techniques such as CNNs, GRUs, and LSTMs to process sequential data. The proposed framework achieved up to 67% precision, 99% recall, 80% F1-score, and 71% AUPR on a dataset, showcasing promising results and opening avenues for further research in this cryptanalysis subarea.

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Section: ) Advanced Encryption Standard (Aes)mentioning

confidence: 99%