Deep Neural Network Based TensorFlow Model for IoT Lightweight Cipher Attack

Tolba, Zakaria; Derdour, Makhlouf

doi:10.1007/978-3-030-96311-8_11

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…in [26], The authors investigate how deep learning can be used to improve on prior classical literature by leveraging ciphertext pair factors to achieve maximum data retrieval with reduced criteria by using convolution neural architectures to observe the correlation among permutable atoms to produce plain text from ciphered text with no Pbox knowledge. They also performed numerous experiments with deep learning models [27][28] [29].…”

Section: Related Workmentioning

confidence: 99%

Machine learning based cryptanalysis techniques: perspectives, challenges and future directions

Tolba

Derdour

Dehimi

2022

2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS)

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The field of cryptanalysis has lately witnessed considerable advancement due to the need for artificial intelligence technologies to simplify the complicated task of vulnerability assessments for cryptographic algorithms. The use of well-known tools such as machine learning and deep learning has piqued the interest of researchers and experts in the field because it has supported research work in discovering great knowledge on the strong and weak points of cryptographic techniques while ushering in the era of automated and AI-driven cryptanalysis.Despite the positive solutions obtained through using DL in the realm of cryptanalysis, it is not without drawbacks. This paper emphasizes the issues encountered when using ML and DL in cryptanalysis as well as new paths of DL with the advent of the quantum neural network approach, which can provide better answers and hence the relevant state of the art.

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Section: Related Workmentioning

confidence: 99%