On the Construction and Cryptanalysis of Multi-Ciphers

In this compilational work, we combine various techniques from classical cryptography and steganography to construct ciphers that conceal multiple plaintexts in a single ciphertext. We name these multi-ciphers . Most notably, we construct and cryptanalyze a Four-In-One-Cipher: the first cipher which conceals four separate plaintexts in a single ciphertext. Following a brief overview of classical cryptography and steganography, we consider strategies that can be used to creatively combine these two fields to construct multi-ciphers. Finally, we cryptanalyze three multi-ciphers which were constructed using the techniques described in this paper. This cryptanalysis relies on both traditional algorithms that are used to decode classical ciphers and new algorithms which we use to extract the additional plaintexts concealed by the multi-ciphers. We implement these algorithms in Python, and provide code snippets. The primary goal of this work is to inform others who might be otherwise unfamiliar with the fields of classical cryptography and steganography from a new perspective which lies at the intersection of these two fields. The ideas presented in this paper could prove useful in teaching cryptography, statistics, mathematics, and computer science to future generations in a unique, interdisciplinary fashion. This work might also serve as a source of creative inspiration for other cipher-making, code-breaking enthusiasts

How to exploit fitness landscape properties of timetabling problem: A newoperator for quantum evolutionary algorithm

© 2020 Elsevier Ltd. All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1016/j.eswa.2020.114211The fitness landscape of the timetabling problems is analyzed in this paper to provide some insight into theproperties of the problem. The analyses suggest that the good solutions are clustered in the search space andthere is a correlation between the fitness of a local optimum and its distance to the best solution. Inspiredby these findings, a new operator for Quantum Evolutionary Algorithms is proposed which, during the searchprocess, collects information about the fitness landscape and tried to capture the backbone structure of thelandscape. The knowledge it has collected is used to guide the search process towards a better region in thesearch space. The proposed algorithm consists of two phases. The first phase uses a tabu mechanism to collectinformation about the fitness landscape. In the second phase, the collected data are processed to guide thealgorithm towards better regions in the search space. The algorithm clusters the good solutions it has foundin its previous search process. Then when the population is converged and trapped in a local optimum, itis divided into sub-populations and each sub-population is designated to a cluster. The information in thedatabase is then used to reinitialize the q-individuals, so they represent better regions in the search space.This way the population maintains diversity and by capturing the fitness landscape structure, the algorithmis guided towards better regions in the search space. The algorithm is compared with some state-of-the-artalgorithms from PATAT competition conferences and experimental results are presented.Peer reviewe