计算机科学
云计算
数据完整性
云存储
保密
计算机安全
数据挖掘
操作系统
作者
L. Remegius Praveen Sahayaraj,S. Muthurajkumar
出处
期刊:Journal of Intelligent and Fuzzy Systems
[IOS Press]
日期:2023-07-07
卷期号:45 (3): 4669-4687
被引量:1
摘要
Preserving the integrity of log data and using the same for forensic analysis is one of the prime concerns of cloud-oriented applications. Since log data collates sensitive information, providing confidentiality and privacy is of at most importance. For data auditors, maintaining the integrity of the log data is a prime concern. Existing models focus on providing models and frameworks that relies on any third-party entity or the cloud service provider (CSP) to handle the logs, which lacks in securing the integrity due to the presence of the external entities. Sole dependence on CSP is a major flaw together with a drawback, since the CSP itself is prone to data theft alliance. In this paper, we instantiate a mechanism which maintains the integrity of the log without compromising the performance efficiency of the system. The influence of machine learning classification techniques is leveraged in order to efficiently classify the log data before it is processed. Progressively the log data integrity is maintained through the proposed Propagated Chain of Log Blocks (PCLB), the Hybrid Vector Committed BST (HVCBST) and lightweight Multikey Hybrid Storage (MKHS) structures. The results of the implemented systems have proven to be efficient and tamper proof compared to the existing systems and can be easily rendered in any private or public cloud deployments.
科研通智能强力驱动
Strongly Powered by AbleSci AI