Stochastic Gradient Descent long short-term memory based secure encryption algorithm for cloud data storage and retrieval in cloud computing environment

云计算 计算机科学 加密 计算机安全 入侵检测系统 云安全计算 数据安全 计算机网络 分布式计算 操作系统
作者
M. Suganya,T. Sasipraba
出处
期刊:Journal of Cloud Computing [Springer Nature]
卷期号:12 (1) 被引量:16
标识
DOI:10.1186/s13677-023-00442-6
摘要

Abstract 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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