Résumé:
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 crucial. In this perspective, encryption consolidates effective technologies that can effectively fulfill these requirements by protecting public
information exchanges. To achieve these aims, the researchers used a wide assortment of encryption algorithms to accommodate the varied requirements of this field, as well as focusing
on complex mathematical issues during their work to substantially complicate the encrypted
communication mechanism. as much as possible to preserve personal information while
significantly reducing the possibility of attacks. Depending on how complex and distinct
the requirements established by these various applications are, the potential of trying to
break them continues to occur, and systems for evaluating and verifying the cryptographic
algorithms implemented continue to be necessary.
The best approach to analyzing an encryption algorithm is to identify a practical and efficient
technique to break it or to learn ways to detect and repair weak aspects in algorithms, which
is known as cryptanalysis. Experts in cryptanalysis have discovered several methods for
breaking the cipher, such as discovering a critical vulnerability in mathematical equations to
derive the secret key or determining the plaintext from the ciphertext.
There are various attacks against secure cryptographic algorithms in the literature, and the
strategies and mathematical solutions widely employed empower cryptanalysts to demonstrate their findings, identify weaknesses, and diagnose maintenance failures in algorithms.
The establishment of artificial intelligence models and approaches to provide a measure of
the relative degree of protection for cryptographic systems represents one of the most recent
strategies implemented in this field. This thesis research expanded on work through an
automated testing strategy that comprises artificial intelligence methodologies to empower
machine learning models to synthesize meaningful patterns and sensitive information from
the ciphertext. Unlike classical cryptanalysis, the goal is to accomplish an automated and
effective set of experiments without any human intervention.
The added value of our contribution is to enrich the results of previous research which
addresses the shortcomings by evaluating P-box permutation algorithms using deep learning.
So that we investigate the benefits of deep learning cryptanalysis techniques in the evaluation
process, using the features of convolutional neural network in black box attack to detect
association between interchangeable entities in order to extract the plaintext effectively and
efficiently from the ciphertext without any P-box knowledge. This thesis also deals with the
study of the security of lightweight encryption used in the Internet of Things through the
realization of several experiments and conclusions.
