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Design and Implementation of a Web-DNA-Based Security Provider Using Python

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Advanced Machine Learning Technologies and Applications (AMLTA 2021)

Abstract

In this paper, we will combine cryptography and steganography to get a high level of security. We will also apply the idea of utilizing DNA computing in cryptography and steganography fields. First, we will briefly present a new DNA based stream cipher algorithm as a cryptographic and stenographic algorithm. This algorithm based on using a simple chaotic equation to generate random sequences which can be used to generate OTP Key from DNA sequences,to divide message to different sequences and to hide data in different DNA files. Secondly, we will briefly present two algorithms for hiding data. The first algorithm is based on spreading each data block in a random file selected randomly from the DNA database utilized for hiding. The second algorithm depends on collecting all the blocks in one block, making the rotation process on it, and then split every five characters in a random file selected from the previous database. Thirdly, we propose a design and an implementation of a Web- D.N.A. - Based Security Provider. The main objective of the website is to work as a security provider web application. So, it provides essential security services like encryption, confidentiality, authentication, data integrity and non-repudiation. Each service has its algorithms. We add two changes to these services’ algorithms. The first change is using random One-Time Pad (OTP) keys with different lengths we choose from a free DNA sequence database. This feature applied only on symmetric cryptography algorithms. The second change is hiding the result of the encryption in another DNA sequences database. Our web site, also, provides other services which will be explained in details.

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Correspondence to Kamel H. Rahouma .

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AbdelGhany, F.M., Rahouma, K.H., Hassan, Y.B., Mahdy, L.N. (2021). Design and Implementation of a Web-DNA-Based Security Provider Using Python. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_106

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