No abstract

Stage of networking is quintessential task in which security comes into play. Securing these networks which contains confidential digital data that needs to secured will be the agenda of cryptography. Many cryptographic algorithms increment their strengths over parameters like key size, increasing the rounds of iteration and finally using confusion box as S-box as it has best robustness. So, this paper mainly focusses over securing digital data with the help of S-box function over Data Encryption Standard (DES) algorithm. For this, a plain text and key will be given to this DES as it extracts 8x8(64) bit characters from the key and converting them into its corresponding ASCII value and are concatenating to form an 8 value by taking mod16. These will give to 8 S-box in order to generate its corresponding output to make even more secure and also shows dynamic DES gives much result than other crypto methods. The evaluation of this integrated s-box and DES shows much fruitful results over factors like non-linearity, Avalanche criterion, Balance, Robustness to linear cryptanalysis, Robustness to differential cryptanalysis.

Biosensors calculate the expression pattern of multiple genes in experimental work. A unique genomic chip is possible to produce levels of expression from multiple genes. The ability to interpret these high-dimensional samples fuels the creation of methods of automated analysis.Even though the existing methods undergo imbalanced problems and less classification accuracy over gene expression datasets.Therefore, a novel computational method has been developed inorder to increase the classification performance of gene expression dataset and accurate disease prediction.By adding fuzzy memberships, we take into account the features of imbalanced data. Within our work, both the sample entropies and the expense for each class decide the fuzzy memberships in order to understand the different samples with various contributors to the judgment boundary. Thus, on imbalanced genomic datasets, the current proposed approach will result in more desirable classification outcomes. In addition, to build a new algorithm, we integrate the fuzzy memberships into current MKL. The results show that the proposed approach will tackle the imbalanced problem and achieve high accuracy rate.

Visual Cryptography (VC) is a technique that is gaining its importance in the modern era for sharing secret images. In conventional visual cryptography schemes, secret shares are generated from secret image itself. The proposed work initially creates a cover image based on a key. Feature images are extracted from these cover images. Secret color image is divided into n shares. Each of these n shares is encrypted using the extracted feature image. It is then encoded into Quick Response code for additional security and send to the receiver. We can retrieve the secret image using k secret encrypted shares since we use (k,n) secret sharing algorithm. The method is most useful in sharing passwords in joint bank accounts.