Performance Enhancement in Cloud Based Network Using Polynomial Encryption and Deep Learning

Authors

  • Ajit Singh, Geeta Rani, Saya

Keywords:

Cloud Computing, Polynomial Encryption, Security, Confusion Matrix, Accuracy parameters, Deep Learning.

Abstract

Present paper provides the conceptual framework for enhancement of performance in cloud -based network using the concepts of polynomial encryption and deep learning. The earlier techniques like RSA, AES and DES etc. used for security systems were slow and provided limited security. In order to enhance the performance and safety of cloud servers, polynomial encryption mechanism with the concept of deep learning has been developed by modifying the existing data encryption techniques to allow novel hybrid cryptography processes. This proposed versatile security mechanism is capable to deal with denial of service, brute force attack and man in middle attack and also capable to classify different type of attacks for the protection of cloud-based networks. The deep learning approach used to restrict invalid data transmission along with data encryption. Further, the mathematical calculations shows that the proposed mechanism provides 85% accuracy whereas the conventional mechanism having accuracy of 75%. Similarly, the value of other parameters like security, precision, recall value and F1-Score of proposed mechanism are better than existing conventional mechanism. Hence, system based on the proposed mechanism is having more efficient, flexible and scalable than the existing one.

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Published

26.03.2024

How to Cite

Ajit Singh. (2024). Performance Enhancement in Cloud Based Network Using Polynomial Encryption and Deep Learning . International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3698 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6097

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Section

Research Article