Open AccessModification of variably modified permutation composition (vmpc) algorithm genetic key algorithm for data security
Author(s) -
Elvis Sastra Ompusunggu,
. Sawaluddin,
Erna Budhiarti Nababan
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/725/1/012123
Subject(s) - algorithm , ciphertext , computer science , permutation (music) , key (lock) , running key cipher , cipher , encryption , stream cipher , block cipher , random permutation , rc4 , cryptography , mathematics , computer network , physics , geometry , computer security , acoustics , block (permutation group theory)
Cryptography is the study of mathematical techniques related to information security aspects such as validity, data integrity, and data authentication. In this study, Variably Modified Permutation Composition (VMPC) algorithm is modified by adding complementary methods to the encryption and decryption process. Keys that are generated randomly will be optimized using Genetic Algorithm. The Variably Modified Permutation Composition (VMPC) algorithm is a symmetrical stream cipher algorithm similar to the RC4 cipher designed by Bartosz Zoltak. The Variably Modified Permutation Composition (VMPC) algorithm is an extension of the one-way function VMPC that was developed into a byte-based encryption algorithm. In its use the VMPC is generated by an 8-bit stream of 256 element permutations. The initial state of the permutation is calculated in VMPC Key Scheduling. The Genetic Algorithm produces a unique intermediate key for each algorithm run. The intermediary key is combined with the Ciphertext in the first level which produces the second level Ciphertext, up to the third level Ciphertext. Attackers will not be able to carry out attacks such as brute force, differential attacks or statistical attacks without having knowledge of the key. From the results of the tests carried out, the more characters that are encrypted and decrypted, the longer it will take. On testing the avalanche effect produces an average value of 51.25% for keys without optimization while 53.42% for keys optimized. The effect of key optimization using Genetic Algorithm has increased the value of the avalanche effect and has an effect on the results of changing bits in the ciphertext.