A creative approach to understanding the hidden information within the business data using Deep Learning
Section snippets
Introduction to Text Steganalysis
The increasing demand for internet facilities in maintaining business records has explored security as a significant attention factor (Ahvanooey et al., 2020). Presently, text steganalysis has been extensively developed to perform the business world's hidden data processing, which enhances information security (Alazab et al., 2018). Text steganalysis is used to express the hidden information in the business data's actual messages (Manogaran et al., 2018). The embedment of such a process lies
Background Study
Taleby Ahvanooey et al. (Yang et al., 2020) aimed to discuss Web Text Security Analysis's reliability using Steganalysis (WTSAS). A study was conducted in small scale business industries. A possible improvement in results was obtained. Since the study sample is too small, the researchers cannot get a valid conclusion about the study.
Yang et al. (Singhal and Bedi, 2020) created a Prominent Steganalysis Hidden Information Sharing (PSHIS) based on several economic and business conditions. It is a
Deep neural network-based invisible text steganalysis (DNNITS)
In the modern business world, information is important in a secure and private organization to maintain confidentiality. Details should be available from the security perspectives when necessary. Attackers can alter the information resulting from the unavailability of information. From a security perspective, violators and encryption technology do not allow information readable and should convert the plain text to encoded text. Encryption technology is converting data into a hidden code that
Results and Discussion
As mentioned above, the proposed system has been executed in an effective online simulation business model. The method is compared with the system of security analysis and word text extraction analysis. Combinations of the semantic features and the syntactic meaning are used and compared with all approaches. For general extraction, the order of resemblance, illustration, and output in terms of execution period, comparisons are carried out. Besides, performance tests and measures for business
Conclusion
This study introduces a deep neural network-based invisible text steganalysis (DNNITS) for business data hiding and retrieval. The new system addresses the limitations of current programs. It is focused on word embedding to choose the best hidden texts using steganalysis. A syntactic and semantic structure is built by the text utilizing the information base, followed by the test in the business archive and the user's language. Assessment results reveal that the method introduced has performed
Author Statement
Conception and design of the study: Yuanfeng Luo acquisition of data: Yue Mo analysis and interpretation of data: Baoji Xie, Guijun Yang
Drafting the manuscript: Chuantao Yao
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