Marghny Mohamed¹, Abeer Al-Mehdhar² and Mohamed Bamatraf¹, ¹Assiut University, Egypt and ²Hadhramout University of Science and Technology, Yemen

Data security is one of major challenges in the recent literature. Cryptography is the most common phenomena used to secure data. One main aspect in cryptography is creating a hard to guess cipher. Artificial Neural Networks (ANN) is one of the machine learning techniques widely employed in several fields based on its characters, depending on the application area. One of these fields is data security. The state of art in this paper is the use of self organizing map (SOM) algorithm concept as a core idea to construct a pad; this pad is used to generate the cipher at one end. At the other end of communication the same process is synchronized to generate the same pad as the deciphering key. The security of the proposed model depends on the complex nature of ANN's. The algorithm could be categorized under symmetric cryptography, merging both stream and block cipher. A modified version of the same algorithm also presented employs permutation and variable SOM neighborhoods. The proposal can be applied over several file formats like videos, images, text files, data benchmarks, etc as show in experimental results.

Self organizing map, Cryptography, Steganography, permutation, block cipher, stream cipher.