Watchword-Oriented and Time-Stamped Algorithms for Tamper-Proof Cloud Provenance Cognition

TL;DR

This paper introduces watchword-oriented and timestamp-based algorithms to enhance the accuracy and security of cloud provenance detection, aiding digital forensic investigations with high detection rates and malicious request rejection.

Contribution

It presents novel algorithms tailored for cloud provenance cognition that improve detection accuracy and security against malicious access.

Findings

Detection rate of 89.33%

Miss rate of 8.66%

Rejects 64% of malicious requests

Abstract

Provenance is derivative journal information about the origin and activities of system data and processes. For a highly dynamic system like the cloud, provenance can be accurately detected and securely used in cloud digital forensic investigation activities. This paper proposes watchword oriented provenance cognition algorithm for the cloud environment. Additionally time-stamp based buffer verifying algorithm is proposed for securing the access to the detected cloud provenance. Performance analysis of the novel algorithms proposed here yields a desirable detection rate of 89.33% and miss rate of 8.66%. The securing algorithm successfully rejects 64% of malicious requests, yielding a cumulative frequency of 21.43 for MR.