Active-Threaded Algorithms for Provenance Cognition in the Cloud preserving Low Overhead and Fault Tolerance

TL;DR

This paper introduces active-threaded algorithms for provenance detection in cloud systems, achieving low latency and fault tolerance to support digital forensic investigations effectively.

Contribution

It presents novel active-threaded algorithms that enable high-speed, low-overhead provenance detection with encapsulation capabilities in cloud environments.

Findings

Mean delay of 8.198 seconds, below the 10-second benchmark

Standard deviation of delay is 1.434 seconds

Cumulative frequency of delays at 45.1%

Abstract

Provenance is the derivation history of information about the origin of data and processes. For a highly dynamic system such as the cloud, provenance must be effectively detected to be used as proves to ensure accountability during digital forensic investigations. This paper proposes active-threaded provenance cognition algorithms that ensure effective and high speed detection of provenance information in the activity layer of the cloud. The algorithms also support encapsulation of the provenance information on specific targets. Performance evaluation of the proposed algorithms reveal mean delay of 8.198 seconds that is below the pre-defined benchmark of 10 seconds. Standard deviation and cumulative frequencies for delays are found to be 1.434 and 45.1% respectively.