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Related Experiment Video

Updated: Aug 16, 2025

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
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A deep learning based steganography integration framework for ad-hoc cloud computing data security augmentation using

Ahmed A Mawgoud1, Mohamed Hamed N Taha1, Amr Abu-Talleb2

  • 1Information Technology Department, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt.

Journal of Cloud Computing (Heidelberg, Germany)
|December 26, 2022
PubMed
Summary

This study enhances cloud security using deep learning-based steganography in ad hoc cloud systems. The novel approach improves data and image concealment against attacks, outperforming existing cloud platforms.

Keywords:
Ad-hoc systemCloud computingCloud securityDeep learningEncryptionSteganography

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Cloud computing adoption accelerated due to the need for remote work, increasing security and privacy concerns.
  • Existing steganography methods using deep learning have shown limited success in enhancing data hiding.

Purpose of the Study:

  • To develop an improved steganography technique for ad hoc cloud systems using deep learning.
  • To enhance the security and privacy of data transmission in cloud environments.

Main Methods:

  • Phase 1: Established an "Ad-hoc Cloud System" using V-BOINC.
  • Phase 2: Implemented a modified steganography and deep learning model for secure data transmission.
  • Integrated data images within colored images for covert transmission.

Main Results:

  • The proposed deep steganography approach demonstrated high effectiveness in concealing data and images against various attacks.
  • The ad hoc cloud system showed superior performance compared to Amazon EC2.
  • The systematic steganography model achieved lower message detection rates.

Conclusions:

  • Deep learning-based steganography offers a promising solution for enhancing data security in ad hoc cloud systems.
  • The developed method provides a robust and efficient way to protect sensitive information in cloud environments.