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Artificial intelligence driven approach for securing backup data and enhancing cyber resilience in sustainable smart infrastructure
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  • Published: 16 March 2026

Artificial intelligence driven approach for securing backup data and enhancing cyber resilience in sustainable smart infrastructure

  • Basant Kumar1,2,
  • Shashi Kant Gupta1,3,
  • Rashmi Dwivedi4,5,
  • Deema Mohammed Alsekai6,
  • Diaa Salama AbdElminaam7,8 &
  • …
  • Ozlem Kilickaya9 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Energy science and technology
  • Engineering
  • Mathematics and computing

Abstract

A crucial factor for smart cities, which are more vulnerable to cyber threats, is Cyber Resilience (CR). Nevertheless, the conventional frameworks didn’t concentrate on assuring the Backup Data (BD) integrity before restoration, showing less resilience. Therefore, this article implements an AI-powered BD integrity verification approach for CR in smart infrastructure using Murmur Polytopes Hash (MPH). Initially, the nodes are initialized in the smart city, followed by node clustering, data security, and storage (cloud server and Interplanetary File System (IPFS) (backup)). Now, the hash code is generated and updated in the Merkle tree. Besides, to perform data collection, pre-processing, clustering, correlation heatmap generation, feature extraction, and attack classification, the proposed ransomware attack detection module is designed. If the data is attacked, then the BD verification is done using MPH. Then, the BD is restored. If the data is normal, then the data is downloaded from the cloud server. Thus, the proposed work had a high security level and accuracy of 98.45% and 98.65%, respectively, showing better resilience.

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Data availability

The datasets generated and/or analysed during the current study are available in the Dataset: https://www.kaggle.com/datasets/amdj3dax/ransomware-detection-data-set

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Acknowledgements

The authors would like to acknowledge the support of Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2026R435), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Funding

This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2026R435), Princess Nourahbint Abdulrahman University, Riyadh, Saudi Arabia.

Author information

Authors and Affiliations

  1. Lincoln University College, 47301, Petaling Jaya, Selangor, Malaysia

    Basant Kumar & Shashi Kant Gupta

  2. Modern College of Business and Science, Muscat, Oman

    Basant Kumar

  3. Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology., Chitkara University Institute of Engineering and Technology, Rajpura, Punjab, 140401, India

    Shashi Kant Gupta

  4. Muscat University, Muscat, Oman

    Rashmi Dwivedi

  5. Alliance University, Bangalore, India

    Rashmi Dwivedi

  6. Department of Information Technology,College of Computer and Information Sciences , Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia

    Deema Mohammed Alsekai

  7. Faculty of Computing and IT, Sohar University, Sohar, Oman

    Diaa Salama AbdElminaam

  8. Jadara Research Center, Jadara University, Irbid, 21110, Jordan

    Diaa Salama AbdElminaam

  9. University of the People, Pasadena, USA

    Ozlem Kilickaya

Authors
  1. Basant Kumar
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Contributions

Formal Analysis-Basant Kumar, Shashi Kant Gupta and Ozlem Kilickaya; Resources, Design and coding—Rashmi Dwivedi; Wrting-Original Draft- Diaa Salama AbdElminaam; Writing-Deema Mohammed Alsekait; Review and Editing- Basant Kumar.

Corresponding authors

Correspondence to Deema Mohammed Alsekai, Diaa Salama AbdElminaam or Ozlem Kilickaya.

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The authors declare no competing interests.

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Cite this article

Kumar, B., Gupta, S.K., Dwivedi, R. et al. Artificial intelligence driven approach for securing backup data and enhancing cyber resilience in sustainable smart infrastructure. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37802-6

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  • Received: 24 October 2025

  • Accepted: 27 January 2026

  • Published: 16 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-37802-6

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Keywords

  • Cyber-security (CS)
  • Artificial intelligence (AI)
  • Cyber resilience (CR)
  • Green smart infrastructure (GSI)
  • Backup data integrity (BDI)
  • Ransomware attack (RA)
  • Energy efficiency (EE)
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