What is it about?
The article is about maintaining the integrity of the log data in cloud environment. It applies the techniques of cryptography and customised blockchain. 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 cum 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 light weight 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.
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Why is it important?
Blockchain is a trending topic in the research world. Creating a customised chain to increase the security of data in a cloud environment is vital.
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This page is a summary of: Efficient classification and preservation of log integrity through propagated chain in cloud, Journal of Intelligent & Fuzzy Systems, August 2023, IOS Press,
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