期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2020
卷号：98
期号：15
页码：2990-2999
出版社：Journal of Theoretical and Applied
摘要：The Rivest Shamir Adleman (RSA) algorithm is one of the cryptographic algorithms that have a high level of security in the message security, and this is due to the difficulty of finding the prime number factor of a huge integer (factor n being the two main factors, p, and q so that it becomes n = p ∙ q). Because it is assessed as safe in securing messages, researchers look for weaknesses in the RSA algorithm. The downside of this RSA algorithm is from the key solving technique. The algorithm for solving this key is sought and studied so that the secret key p and q can be known or cryptanalysis. The key solving technique that can be done is to use the heuristic method, which is a genetic algorithm. The process of finding factors from this public key is conducted algorithmically using a genetic algorithm. The genetic algorithm works by factoring the public key n randomly to generate the initial value of the chromosomal candidates p and q. After the initial population is formed, the chromosome will experience evaluation, selection, crossover, and mutation so that the best solution is found. The chromosomes are produced from one generation to a maximum of a predetermined generation. Furthermore, two figures of the results of the factorization must be done checking the prime number, in the study of the test method of prime numbers using the Lehmann algorithm. If the test result of the prime number is correct, then the two numbers are the secret key p and q of the RSA algorithm. The results of the research from solving the technique of public-key RSA algorithms using genetic algorithms suggest that the genetic algorithm can be used to break the public key of the RSA algorithm. The opportunity to find the secret key p and Q RSA algorithm is affected by the size of the pop size and maximum size of the generation, the larger the size of population size and maximum size of the generation, the larger the population size and maximum size of the generation then the higher the probability of the p and Q secret keys found. The size of the crossover Rate (PC) is best used to solve the problem of solving the RSA public key using the genetic algorithm is 25%-50%. While the size of the mutation rate (PM) is best used to resolve the problem of solving the RSA public key, using the genetic algorithm is 10%-20%.
