Cryptanalysis and improvement of multimodal data encryption by machine-learning-based system
Zakaria Tolba

TL;DR
This paper discusses the importance of cryptanalysis in evaluating and improving encryption algorithms used in secure data transmission, highlighting recent methods and challenges in breaking and strengthening cryptographic systems.
Contribution
It introduces a machine-learning-based system for cryptanalysis of multimodal data encryption, proposing improvements to enhance security and detect vulnerabilities.
Findings
Identified vulnerabilities in existing encryption algorithms.
Demonstrated effectiveness of machine learning in cryptanalysis.
Proposed enhancements reduce attack success rates.
Abstract
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…
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Taxonomy
TopicsChaos-based Image/Signal Encryption
