CULTIVATING RESILIENCE: A TRANSFORMATIVE APPROACH TO ENHANCING CLOUD DATA SECURITY WITH TRANSFORMER-BASED TECHNIQUES
| Autor: | Gantela Prabhakar, Bobba Basaveswara Rao |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Proceedings on Engineering Sciences, Vol 6, Iss 4, Pp 1551-1560 (2024) |
| Druh dokumentu: | article |
| ISSN: | 2620-2832 2683-4111 |
| DOI: | 10.24874/PES06.04.014 |
| Popis: | Data centres have grown drastically in size and in number as the digital economy has proliferated. For the advancement of society and the economy, data centres are becoming increasingly important. But even a little period of data centre downtime can be extremely harmful. Secure management of the physical infrastructure of data centres is essential to resolving this problem. A decentralized approach to healthcare systems is also made possible by blockchain technology, which gets rid of some of the drawbacks of centralized systems like single points of failure. Currently, a number of enhanced resilience security solutions using blockchain and ANP (Analytical Neural Processes) techniques have been presented to improve the security of transformation-based technologies. ANP finds false data and recognizes harmful data measured by medical sensors. For the Internet of Things (IoT) and Cyber Physical Systems (CPS), the development of defences against diverse cyber threats is advancing. Leveraging cloud environments to discover harmful code may not be a practical strategy in the future as malicious code grows in prevalence and there are no established techniques for identifying malicious code. Therefore, before the fog layer processes the data, transformation-based systems can identify and stop cyber-attacks. Additionally, it makes use of a blockchain network at the fog layer to guarantee data integrity and privacy by preventing data modification. Experimental findings demonstrate that the ANP and block chain models deliver what is promised. Additionally, the Transformer Neural Network (TNN) model's accuracy is 99.99% according to the F1 score accuracy indicator. |
| Databáze: | Directory of Open Access Journals |
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