الفهرس 
A deep steganography approach to secure data transmission in ad-hoc cloud systems
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Abstract
In the early days of digital transformation, the automation, scalability, and
availability of cloud computing made a big difference for business. Nonetheless,
significant concerns have been raised regarding the security and privacy levels that
cloud systems can provide, as enterprises have accelerated their cloud migration
journeys in an effort to provide a remote working environment for their employees,
primarily in light of the COVID-19 outbreak. The goal of this thesis is to come up with
a way to improve steganography in ad hoc cloud systems by using deep learning. This
research implementation is divided into two phases. In phase 1, the ”Ad-hoc Cloud
System” idea and deployment plan were set up with the help of V-BOINC. In Phase 2,
a modified form of steganography and deep learning were used to study the security
of data transmission in ad-hoc cloud networks. In the majority of prior studies,
attempts to employ deep learning models to augment or replace data-hiding systems
did not achieve a high success rate. The implemented model inserts data images
through colored images in the developed ad-hoc cloud system. A systematic
steganography model conceals from statistics lower message detection rates. In
addition, it may be necessary to embed small images beneath large cover images. The
implemented ad-hoc system has delivered a well-stable performance compared to
Amazon AC2, and the execution of the proposed deep steganography approach
provided a high rate of evaluation in concealing both data and images when tested
against various attacks in an ad-hoc cloud system environment.