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.
Previous provenance models have assumed that there is complete certainty in the provenance relationships. But what if this assumption does not hold? In this work, emaiwe propose a probabilistic provenance graph (PPG) model to characterize scenarios where provenance relationships are uncertain. We describe two motivating examples. The first example demonstrates the uncertainty associated with the provenance of an email. The second example demonstrates and characterizes the uncertainty associated with the provenance of statements in documents.