Machine learning-based D2D communication for a cloud-secure e-health system and data analysis by feature selection with classification

Awasthi, Aishwary; Suchithra, R; Chakravarty, Ajay; Shah, Jaymeel; Ghosh, Debanjan; Kumar, Avneesh

doi:10.21203/rs.3.rs-2653343/v1

Cited by 1 publication

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Awasthi et al [15] suggest a new approach to e-health data analysis using cloud-based device-to-device communication through feature selection and categorization. The goal of this study is to examine the potential of integrating cloud and distributed computing into e-healthcare by conducting a thorough requirement investigation and user survey.…”

Section: Related Workmentioning

confidence: 99%

Design of Mutual Authentication Method for Deep Learning Based Hybrid Cryptography to Secure Data in Cloud Computin

Mohd,

Kummarikunta,

Naga

et al. 2023

IJSSE

View full text Add to dashboard Cite

In today's highly competitive environment, it might be difficult to keep sensitive data or information safe. Sharing sensitive information between parties in a cloud environment requires a high level of trust between them. There are numerous methods for achieving data and information security, including cryptography, steganography, etc. This research introduces a new approach to mutual authentication by using a pre-trained model of a convolutional neural network (CNN) to identify malicious activity on the internet. In this research paper, we present a novel approach for enhancing the security of data in cloud computing environments through the design of a mutual authentication method for deep learning-based hybrid cryptography. Our approach combines the strengths of hybrid cryptography and the power of deep learning to provide a robust and adaptable solution for securing data in the cloud. One of the key innovations of our approach is the integration of a pre-trained convolutional neural network (CNN) model. This CNN plays a pivotal role in identifying and mitigating malicious activities on the internet that could pose a threat to cloud-based data. By continuously monitoring network traffic and data patterns, the CNN contributes to the proactive defense mechanism of our system. Secure communication between the involved parties is ensured by combining cryptography with authentication for key agreements. However, no known security method has simultaneously provided a high level of security and a fast execution time. When compared to older encryption systems, hybrid encryption techniques are far superior in terms of providing peace of mind for users. In order to provide robust security, this paper presents hybrid encryption procedures (HEA) by combination of symmetric key (Message Authentication Code [MAC]) and asymmetric key cryptographic procedures (Modified and Enhanced Lattice-Based Cryptography [MELBC]). Results from experiments show that the suggested HEA algorithm offers more security than competing security algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%