The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Stochastic Gradient Descent long short-term memory based secure encryption algorithm for cloud data storage and retrieval in cloud computing environment
Open accessAbstract
With the increasing rise of distributed system technologies, one of the most pressing problems facing the digital world is ensuring the security of sensitive and confidential data during transport and storage, which is also regarded as one of the most critical difficulties facing cloud computing. Numerous techniques exist for enhancing data security in the cloud computing storage environment. Encryption is the most important method of data protection. Consequently, several accessible encryption strategies are utilized to provide security, integrity, and authorized access by employing modern cryptographic algorithms. Cloud computing is an innovative paradigm widely accepted as a platform for storing and analysing user data. The cloud is accessible via the internet, exposing the data to external and internal threats. Cloud Service Providers (CSPs) must now implement a secure architecture to detect cloud intrusions and safeguard client data from hackers and attackers. This paper combines Stochastic Gradient Descent long short-term memory (SGD-LSTM) and Blow Fish encryption to detect and prevent unauthorized cloud access. User registration, intrusion detection, and intrusion prevention are the three phases of the planned system. The SGD-LSTM classifier predicts cloud data access and prevents unauthorized cloud access. In the data access phase, cloud data access is managed by authenticating the authorized user with the Blowfish encryption algorithm. Comparing the proposed classifier to existing classifiers demonstrates that it detects abnormal access accurately. The experimental outcomes enhanced data security, which can be utilized to protect cloud computing applications. The experimental results of the suggested SGD-LSTM algorithm indicated a high level of protection, as well as a considerable improvement in security and execution speed when compared to algorithms that are often used in cloud computing.
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“For example, refs.[20,48,49] explore innovative techniques for scaling threat detection in decentralized architectures, reflecting a shift toward real-world deployment in complex infrastructures.”
From Application of Machine Learning and Deep Learning Techniques for Enhanced Insider Threat Detection in Cybersecurity: Bibliometric Review (2025) · cites this paper as [48]
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Cites
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Machine Learning-Based Models for Early Stage Detection of Autism Spectrum Disorders
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Towards DNA based data security in the cloud computing environment
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Deep Incremental Hashing Network for Efficient Image Retrieval
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Cited by
Comprehensive Review and Analysis of Cryptography Techniques in Cloud Computing
Sasikumar, K., & Nagarajan, S. (2024) · IEEE Access · doi:10.1109/access.2024.3385449 · Cited by 75
Pichandi, K. V., Janarthanan, V., Annamalai, T., & Arumugam, M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124479 · Cited by 29
Securing the Digital Supply Chain Cyber Threats and Vulnerabilities
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Rafi, S. M., Yogesh, R., & Sriram, M. (2024) · Computers & Security · doi:10.1016/j.cose.2024.104183 · Cited by 12
A hybrid deep learning approach for enhanced network intrusion detection
Prabu, K., & Sudhakar, P. (2024) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v33.i3.pp1915-1923 · Cited by 8
Metadata
- Authors
- Suganya, M., & Sasipraba, T.
- Affiliations
- Sathyabama Institute of Science and Technology (IN)
- Corresponding author
- Suganya, M.
- Venue
- Journal of Cloud Computing
- Published
- 2023-05-09
- Volume
- 12
- Issue
- 1
- ISSN
- 2192-113X (ISSN-L)
- OpenAlex
- W4381568774
- Cited by
- 36
- References
- 32
- Citations by year
- 2026: 4 · 2025: 16 · 2024: 14 · 2023: 2
- FWCI
- 8.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Journal of Cloud Computing Advances Systems and Applications (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Keywords
- cloud data security, cloud storage encryption, cloud data retrieval, intrusion detection, intrusion prevention, unauthorized access detection, Blowfish encryption, user authentication, cloud service providers
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- CoChat. (2026). Citation check: Stochastic Gradient Descent long short-term memory based secure encryption algorithm for cloud data storage and retrieval in cloud computing environment. https://app.cochat.ai/papers/10.1186/s13677-023-00442-6
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- Suganya, M., & Sasipraba, T. (2023). Stochastic Gradient Descent long short-term memory based secure encryption algorithm for cloud data storage and retrieval in cloud computing environment. Journal of Cloud Computing. https://doi.org/10.1186/s13677-023-00442-6
