Intelligent Systems for Information Security
Ayman M. Bahaa-Eldin

TL;DR
This thesis explores the use of genetic algorithms and neural networks to enhance cryptanalysis and design of block ciphers, demonstrating improved attack efficiency and proposing a neural network-based cipher.
Contribution
It introduces novel genetic algorithm extensions for differential cryptanalysis and a neural network-based block cipher, advancing cryptographic attack and design methods.
Findings
Genetic algorithm-based attack reduces complexity to less than a quarter of traditional methods.
Neural network-based attack successfully approximates cipher mappings.
Proposed neural network cipher leverages fast mapping and small memory for security.
Abstract
This thesis aims to use intelligent systems to extend and improve performance and security of cryptographic techniques. Genetic algorithms framework for cryptanalysis problem is addressed. A novel extension to the differential cryptanalysis using genetic algorithm is proposed and a fitness measure based on the differential characteristics of the cipher being attacked is also proposed. The complexity of the proposed attack is shown to be less than quarter of normal differential cryptanalysis of the same cipher by applying the proposed attack to both the basic Substitution Permutation Network and the Feistel Network. The basic models of modern block ciphers are attacked instead of actual cipher to prove that the attack is applicable to other ciphers vulnerable to differential cryptanalysis. A new attack for block cipher based on the ability of neural networks to perform an approximation…
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Taxonomy
TopicsChaos-based Image/Signal Encryption · Cryptographic Implementations and Security · Metaheuristic Optimization Algorithms Research
