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العنوان
Cryptanalysis Based On Swarm Intelligence Techniques \
المؤلف
Abd El-Atife, Samah Samir Abd El-Hallim.
هيئة الاعداد
باحث / سماح سمير عبد الحليم عبد اللطيف عبد اللطيف
مشرف / حاتم محمد سيد احمد عبد القادر
مشرف / رزق مسعود رزق الله
مشرف / زينب محمد حسن هنداوي
الموضوع
Embedded Computer Systems. Cryptography. Data Encryption (Computer Science) Artificial Intelligence. Computational Intelligence. Data Protection.
تاريخ النشر
2022.
عدد الصفحات
111 p. :
اللغة
الإنجليزية
الدرجة
ماجستير
التخصص
الهندسة (متفرقات)
تاريخ الإجازة
10/12/2022
مكان الإجازة
جامعة المنوفية - كلية الهندسة - العلوم الاساسية الهندسية
الفهرس
Only 14 pages are availabe for public view

from 114

from 114

Abstract

With the advancement of communication technologies and the vast flow of information through the internet, maintaining the secrecy of transmitted information is becoming increasingly important. The study of diverse strategies for protecting communication between the sender and the recipient is known as cryptography. E-mail, ecommerce, e-learning, health and medicine, retail, and other areas have all benefited from the use of the
internet, But in order to avoid many problems, this communications must be
secured by an encryption method. Cryptanalysis is a method for discovering and developing vulnerabilities in any cryptographic system, so, the cryptanalysis process is very important that is used to identify, exploit, and fix flaws in cryptographic systems. Cryptosystem cryptanalysis is regarded as an NP-Hard task in modern cryptography. The modern cipher has
nonlinearity and low autocorrelation in their structure, traditional techniques, and brute-force attacks against modern ciphers as Simplified Advanced Encryption Standard (S-AES) , stream cipher, and Merkle Hellman cipher) are difficult. In this thesis, a binary modification of the hybridization of Particle Swarm Optimization (PSO) with other algorithms is suggested to solve cryptanalysis for different types of modern cipher. The proposed hybridization algorithms employ two different types of attack (KPA and COA). According to our results, the proposed hybridization algorithms are faster and more accurate than employing each algorithm separately. Experiments revealed that the proposed hybridization algorithms outperform other algorithms in the literature in terms of efficacy and robustness. This proposed algorithms reduced the search space by a massive
amount.