Enhanced Deep Learning-Based Security Model for Data in Cloud

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 3

Year of Publication : 2025

Authors : Gantela Prabhakar, Bobba Basaveswara Rao

10.14445/23488549/IJECE-V12I3P108

How to Cite?

Gantela Prabhakar, Bobba Basaveswara Rao, "Enhanced Deep Learning-Based Security Model for Data in Cloud," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 3, pp. 88-100, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I3P108

Abstract:

Nowadays, numerous cloud services are available for companies of all sizes and types. It can be used for virtual workstations, analysis, backup, and even software development. However, this ease of use is accompanied by security risks. The technology's limitations make information security a major concern for cloud computing. A comprehensive set of technical solutions, policies, and procedures for safeguarding cloud-based systems or applications, as well as user access and data rights, are known as cloud security. Data availability, integrity, and confidentiality are fundamental concepts in information security. For companies of all sizes and types, a variety of cloud services are now available. By sorting the jobs in each data set and choosing the ones with the highest scores, choose only the most pertinent ones. The proposed Convolutional Neural Network (CNN)-BI-LSTM's accuracy was tested, trained on, and validated using the CICDDoS2019 dataset, which had a 94.52% accuracy rate. A Kalman neural network with back-propagation has been utilized to detect Distribution Denial of Service (DDoS) in IoT networks compatible with 5G. The recall rating for this model was the highest (0.9749). The highest accuracy score, 0.954, was achieved by IDS based on convolutional neural networks. Finally, combine a model that more precisely and effectively detects and categorizes DDoS assaults in a multi-control SDN with an entropy-based deep learning approach. According to the experiment's findings, Recurrent Neural Networks (RNN) had an accuracy of 98.6%, Multi-Layer Perceptron (MLP) had 98.3%, Gated Recurrent Unit (GRU) had 96.4%, and LSTM had 99.42%. Among other suggested models, the Long Short-Term Memory (LSTM) demonstrated great accuracy. To address these problems and offer a defense against sophisticated threats, an innovative deep-learning methodology was developed. Many of these issues can be resolved using new ideas and methods in cyber security, such as speech recognition, behavioral anomaly detection, malware, botnet detection, and DDoS detection. We introduce a secure and fair distributed deep learning architecture that solves the problems mentioned above and improves data security in the cloud.

Keywords:

Convolutional Neural Network, Distributed Denial of Service, Multi-Layer Perceptron, Long Short-Term Memory, Back-propagation.

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