Efficient Implementation of AES-256 for Secure Machine Learning Datasets: A Performance and Compatibility Study
Efficient Implementation of AES-256 for Secure Machine Learning Datasets: A Performance and Compatibility Study |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-9 |
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| Year of Publication : 2025 | ||
| Author : Nandini Sharma, Pritaj Yadav | ||
| DOI : 10.14445/22315381/IJETT-V73I9P109 | ||
How to Cite?
Nandini Sharma, Pritaj Yadav,"Efficient Implementation of AES-256 for Secure Machine Learning Datasets: A Performance and Compatibility Study", International Journal of Engineering Trends and Technology, vol. 73, no. 9, pp.91-99, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I9P109
Abstract
The present study explores a method combining machine learning with the AES-256-based encryption to protect the datasets while maintaining the accuracy. The presented approach addresses an increasing need for data security in the context of increased cyber threats that particularly focus on healthcare and finance. The AES-256 is one of the well-known algorithms that is susceptible to attempted attacks and ensures confidentiality throughout the transmission and storage to encrypt datasets. The neural network processes the data and attains an accuracy of 87% for binary classification tasks, which validates the effectiveness and compatibility of the model. Different performance indicators demonstrate the seamless trade-off between security and efficiency, which classifies accuracy and encryption overhead. The paper provides a framework customized for the different businesses that require stringent data protection and highlights the significance of handling data safety.
Keywords
Security, AES-256, Machine Learning, Neural Network, Federated Learning.
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