Journal of Computer Science

Randomness Analysis of DES Ciphers Produced with Various Dynamic Arrangements

Malik Qasaimeh and Raad S. Al-Qassas

DOI : 10.3844/jcssp.2017.735.747

Journal of Computer Science

Volume 13, Issue 12

Pages 735-747


Over the past few years, researchers have devoted efforts to enhance the original DES encryption algorithm. These enhancements focus on improving multiple perspectives of the algorithm through enhancing its internal components to deliver a robust DES variant against different kinds of typical attacks such as linear and differential crypto analysis, in addition to the newly evolved attacks such as differential power analysis attacks. In fact the output of existing solutions have enhanced the ciphertext randomness. This paper introduces two encryption algorithms that enhance the original DES named DDES and HDES. DDES is mainly based on a secure selection of both S-boxes and P-box arrangements during each encryption round, it has also extended the key length by adding two more keys beside the original one to the encryption process. HDES, on the other hand, uses a hash function to generate a random fingerprint for each plaintext block. This fingerprint is used to generate the seed to produce round seeds that are used to select secure S-boxes only for each round in the encryption process. These two variants meet with certain demands that are imposed by the user applications context. DDES provides a higher ciphertext randomness with some added processing time, while HDES provides a relatively secure variant with lower processing time. These two variants can provide alternatives depending on the targeted applications that require different levels of security and processing time. DDES and HDES have been evaluated and compared against DES, DESX and 3DES, using a number of metrics including chi-square test, cipher data difference, hamming distance and processing time.


© 2017 Malik Qasaimeh and Raad S. Al-Qassas. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.