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     Research Journal of Applied Sciences, Engineering and Technology


Steganography Based Secure Data Storage and Intrusion Detection for Cloud Computing Using Signcryption and Artificial Neural Network

1J. Anitha Ruth, 2H. Sirmathi and 1A. Meenakshi
1SRM University
2COE SRM University, Sikkim SRM University, SRM Nagar, Kattankulathur, Kancheepuram District-603 203, Tamilnadu, India
Research Journal of Applied Sciences, Engineering and Technology  2016  5:354-364
http://dx.doi.org/10.19026/rjaset.13.2953  |  © The Author(s) 2016
Received: December ‎9, ‎2015  |  Accepted: April ‎5, ‎2016  |  Published: September 05, 2016

Abstract

An efficient approach for providing very high secure storage data to the cloud system is proposed in this study. In order to effectively access data from the cloud server, a secure authentication is highly essential. After the authentication the user data is examined to ascertain whether the specified data is normal or intrusive in accordance with the intrusive recognition system, such as the Artificial Neural Network (ANN). It is accompanied by the encryption of the file by means of the signcryption technique. For the purpose of enhancing the storage safety of the novel approach, the steganography methods are carried out immediately after the encryption technique. In the innovative technique, the linguistic steganography approach is employed to conceal the data from the TPA. Subsequently, the data is subdivided arbitrarily and is amassed into cloud. The epoch-making steganography based secure storage and intrusion recognition technique is performed with the mighty assistance of the Cloud simulator in the working platform of Java software.

Keywords:

Artificial neural network, Artificial neural network, linguistic steganography, signcryption, steganography,


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Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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