Feature indexing and search optimization for enhancing the forensic analysis of mobile cloud environment

Abstract

ABSTRACT The increased utilization of Mobile Cloud Computing (MCC) technology creates an opportunity for cybercrimes. Modeling the suitable methods for mobile cloud forensic examination and analysis is essential to improve the investigation performance. This paper incorporates data mining and optimization methods to enforce precise handling of the mobile cloud evidence in examination and analysis to improve the investigation performance. It enhances the analysis of the mobile cloud forensics with the incorporation of the evidence indexing, cross-referencing, and keyword searching as the sub-processes. The proposed Forensic Examination and analysis methodology using the Data mining and Optimization (FEDO) approach examines the key features of the evidence and indexes the pieces of evidence with key features to facilitate the investigation over the massive cloud evidence. By analyzing the temporal and geo-information, it applies cross-referencing to alleviate the evidence toward the case-specific evidence. The proposed methodology improves the searching capability of the investigation through the Linearly Decreasing Weight (LDW) strategy based Particle Swarm Optimization (PSO) algorithm. Thus, the experimental results demonstrate that the proposed forensic methodology yields better investigation performance in terms of accuracy of evidence detection.