Original scientific paper

https://doi.org/10.17559/TV-20240920002000

Enhanced Data Protection and Application Delivery in Cloud Computing Using Intelligent Optimization and Encryption

Sathish D. ; Department of Computer Science and Engineering, Sri Ramanathan Engineering College, Tiruppur, Uthukuli Main Road Nadupatti, Tiruppur, 638 056, Tamil Nadu, India *
K. R. Valluvan ; Department of Electronics and Communication Engineering, Velalar College of Engineering and Technology, Thindal, Erode - 638 012, Tamil Nadu, India
S. Jabeen Begum ; Department of Computer Science and Engineering, Velalar College of Engineering and Technology, Thindal, Erode - 638 012, Tamil Nadu, India

* Corresponding author.

Abstract

Cloud computing has become a crucial component of modern IT infrastructure, offering scalability and cost-effectiveness. However, ensuring the security of data and applications in cloud environments remains a significant challenge. To enhance cloud computing security, we propose a novel design that optimizes application delivery using an Intelligent Deer Hunting Optimized Adjustable Long Short-Term Memory (IDAHO-ALSTM) model combined with Triple Data Encryption Standard (3DES) encryption. The IDAHO-ALSTM model adapts resource allocation and routing decisions by learning from historical data and user patterns, thereby maximizing application delivery. For robust security, 3DES is employed for data encryption and decryption, ensuring data integrity and confidentiality. Experimental results demonstrate that the integration of IDAHO-ALSTM and 3DES not only maintains superior security but also enhances the performance of cloud applications. The IDAHO-ALSTM model reduces latency and minimizes resource wastage by dynamically adjusting to changing workloads. A comparative performance analysis indicates that the proposed method outperforms traditional approaches, achieving an Accuracy of 98.2%, Precision of 95%, F-score of 95.6%, Recall of 96.7%, Encryption time of 4 seconds, and Decryption time of 2.6 seconds. These findings suggest that this hybrid solution is suitable for various cloud computing scenarios, providing strong data protection and optimal application delivery. This study contributes to ongoing efforts to enhance the capabilities of cloud computing services concerning application delivery and security. In real-world applications IDAHO-ALSTM model is capable of forecasting possible health problems or identifying unusual patterns in time-series data (such as heart rate, glucose levels, etc.). ALSTM is especially effective in understanding long-term relationships and trends in time-series data, which is crucial for healthcare predictions.

Keywords

application optimization; cloud computing; data security; encryption; long short-term memory (LSTM)

Hrčak ID:

335049

URI

https://hrcak.srce.hr/335049

Publication date:

30.8.2025.

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