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A Novel Approach for Data Security Using DNA Cryptography with Artificial Bee Colony Algorithm in Cloud Computing
Published in Amitoj Singh, Vinay Kukreja, Taghi Javdani Gandomani, Machine Learning for Edge Computing, 2023
Artificial Bee Colony optimization is as warm-based optimization technique that achieves the best accessible value using the fitness function to generate a suitable cryptographic algorithm. In this chapter, DNA cryptography is used for data encryption along with the Artificial Bee Colony algorithm for key generation. DNA computing is the process of encoding alphabets and numbers into the nucleotides bases (As, Ts, Cs, and Gs) and transcripting and transforming them into a complementary and binary form. DNA computing is able to solve many computational problems and provide better performance. This chapter introduces a unique concept of DNA cryptography, with results that are virtually unbreakable and, additionally, manage a part of storage issues. The key is generated using the Artificial Bee algorithm and operations are performed on the encoded text using this key. This text is further re-encrypted by converting into hexadecimal. The decryption process is the exact reverse process of the encryption process.
Localization and Transport of Charge by Nonlinearity and Spatial Discreteness in Biomolecules and Semiconductor Nanorings. Aharonov–Bohm Effect for Neutral Excitons
Published in Sergey Edward Lyshevski, Nano and Molecular Electronics Handbook, 2018
F. Palmero, J. Cuevas, F.R. Romero, J.C. Eilbeck, R.A. Römer, J. Dorignac
Other possible technological applications based on DNA are related to DNA computers and DNA computing. The similarity between the way that DNA works and the operations of a Turing machine, a theoretical device that processes and stores information, suggests the possibility of using DNA to perform computations. In general, DNA computing is similar to parallel computing, taking advantage of different DNA molecules to simultaneously perform different calculations. In this field, the role of information processing in evolution and the possibility to reproduce these issues in a controlled environment was first addressed by Adleman’s experiment, where the so-called Hamiltonian Path problem was set out [10] and, since this experiment, some Turing machines have proven to be constructible; even DNA computers with different input and output modules capable of fighting diseases have been developed [11]. On the other hand, molecular logic gates are crucial for the development of nanocomputers. Logical gates perform basic logic operations, (such as AND, NOT, OR), and in this field DNA have proven to be useful in building them [12,13]. For a review about DNA computing, see [14,15].
How to Untangle Complex Systems?
Published in Pier Luigi Gentili, Untangling Complex Systems, 2018
The basic idea of DNA computing is to exploit (1) the bases of DNA strands to encode information and (2) molecular biology tools to make computations. It was Leonard Adleman (1994) who pioneered the field when he solved a small instance of the Hamiltonian Path Problem (commonly known as the Travelling Salesman Problem) solely by manipulating DNA strands in a test tube. The Hamiltonian Path is an example of NP-complete problem (read Chapter 12): given a graph with a certain number of nodes, i.e., given a map with a certain number of cities, and given a certain number of interconnections between nodes, find the path that starts at the start node and ends at the end node and passes through each remaining node exactly once. So far, no one has proposed an algorithm that gives the exact solution in a short time for maps with many nodes (whose number is indicated by N). There are only algorithms that give approximate solutions, achievable in reasonable time. An algorithm of this kind requires the generation of random paths through the graph. Then, for each randomly generated path, it is necessary to follow the instructions reported in the flowchart of Figure 13.8.
A Comprehensive Literature of Genetics Cryptographic Algorithms for Data Security in Cloud Computing
Published in Cybernetics and Systems, 2023
Ozgu Can, Fursan Thabit, Asia Othman Aljahdali, Sharaf Al-Homdy, Hoda A. Alkhzaimi
Scholars have become increasingly interested in the genetic algorithm approach in recent years. Genetic algorithms (GA) are a derivative-free method for solving optimization problems inspired by evolutionary processes and natural selection concepts. GA treats their inputs like chromosomes and performs various processes similar to the processes in cell nuclei dealing with DNA (such as crossing and mutation). A set of solutions constitutes a population, and the evolution of a population is governed by Darwin’s principle of natural selection, where only the best solutions remain. Genetic algorithm has proved to be an effective optimization technique and has a widespread application in various fields, including business, medicine, science, and engineering. The application of genetic algorithms can also be seen in cryptography (Tahir et al. 2020; Indrasena Reddy, Siva Kumar, and Reddy 2020). The use of genetics computing (DNA, RNA, and mRNA) in cryptography has gained the interest of researchers in recent years. DNA computing performs computations by using chemical processes on living molecules, and it has been demonstrated that rapid computing is possible using DNA molecules. DNA coding is a promising new area in cryptographic research, where cryptographic algorithms are developed using concepts in molecular biology. New encryption techniques that differ from the traditional ones are needed to resist future attacks. DNA coding combines classical techniques with molecular biology concepts to create efficient coding systems that exploit classical coding schemes and solve their limitations (Namasudra, Devi, et al. 2020; Athitha, Akshatha, and Vandana 2014; Goyal and Jain 2016).