Efficient classification and preservation of log integrity through propagated chain in cloud

Abstract

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.