Research on data reliability and storage security verification for cloud platform based on machine learning

Chengjun Wen; Guohong Liu; Fang Ding; Zhendong Li; Haitang Lin

doi:10.1117/12.3031396

19 July 2024 Research on data reliability and storage security verification for cloud platform based on machine learning

Chengjun Wen, Guohong Liu, Fang Ding, Zhendong Li, Haitang Lin

Author Affiliations +

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131814X (2024) https://doi.org/10.1117/12.3031396
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

At present, the data security and reliability of cloud storage are increasingly threatened by unreliable factors, resulting in major data security problems. As the data security of cloud platforms becomes increasingly prominent, this study starts with the current cybersecurity situation and the challenges of cloud data storage, and emphasizes the urgency of developing an efficient and reliable data verification mechanism. Subsequently, we propose a novel type of machine learning model that leverages advanced algorithms to identify and defend against potential threats of data breaches and corruption. In addition, we have verified the effectiveness of the model through a series of rigorous experiments, and the experimental results show that the model can not only accurately identify various security vulnerabilities, but also adapt to different cloud platform environments, and provide a strong guarantee in terms of data integrity and confidentiality.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chengjun Wen, Guohong Liu, Fang Ding, Zhendong Li, and Haitang Lin "Research on data reliability and storage security verification for cloud platform based on machine learning", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131814X (19 July 2024); https://doi.org/10.1117/12.3031396

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available