Open AccessAn improved fitness function for automated cryptanalysis using genetic algorithm
Author(s) -
Md. Shafiul Alam Forhad,
Md. Sabir Hossain,
Mohammad Obaidur Rahman,
Md. Mostafizur Rahaman,
Md. Mokammel Haque,
Muhammad Kamrul Hossain Patwary
Publication year - 2019
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v13.i2.pp643-648
Subject(s) - fitness function , genetic algorithm , computer science , fitness approximation , cryptanalysis , function (biology) , s box , set (abstract data type) , algorithm , artificial intelligence , cryptography , machine learning , block cipher , biology , evolutionary biology , programming language
Genetic Algorithm (GA) is a popular desire for the researchers for creating an automated cryptanalysis system. GA strategy is useful for many problems. Genetic Algorithms try to solve problems by using genetic processes. Different techniques for deciding on fitness function relying on the ciphers have proposed by different researchers. The most necessary component is to set such a fitness function that can evaluate different types of ciphers on the identical scale. In this paper, we have proposed a combined fitness function that is valid for great sorts of ciphers. We use GA to select the fitness function. We have bought the higher result after imposing our proposed method.