A creative approach to understanding the hidden information within the business data using Deep Learning

Abstract

Crucial business data is an essential asset for each business establishment. Data security is vital when sensitive data are transmitted over the Internet in a business environment. Steganography is the art of obscuring data inside a regular file of similar or different forms. For digital forensics, hiding data has always been necessary. The current information hiding method based on deep learning models can not directly use the original data as carriers, which means the method can not use the prevailing data in big data to hide information. Hence, this paper proposes a Deep neural network-based invisible text steganalysis (DNNITS) for business data hiding. This paper uses a word embedding layer to extract the syntax and semantic word features. A rough set of relative information entropy has been employed based on information features, and the optimized feature matrices are determined. The information in the optimized feature matrices are weighted, and the hiding information weighted feature is acquired. The findings reveal that our model can safely hide secret messages conveniently, quickly, and with no restriction on the business environment's data amount. The experimental results show that the suggested DNNITS model enhances the extraction rate of 95.4%, significance rate of 97.5%, the performance ratio of 89.6%, an efficiency ratio of 98.7%, recall ratio of 90.4%, and the lower error rate of 10.2% compared to other existing models.