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Artificial intelligence driven approach for securing backup data and enhancing cyber resilience in sustainable smart infrastructure – Scientific Reports
- Article
- Open access
- Published:
- 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
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.
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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.
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.
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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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DOI: https://doi.org/10.1038/s41598-026-37802-6
