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

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 me...

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Bibliographic Details
Published in:Information security journal. 2021-07, Vol.30 (4), p.235-256
Main Authors: Alnajjar, Ibrahim Ali, Mahmuddin, Massudi
Format: Article
Language:English
Subjects:
Algorithms
Cloud computing
cross-referencing
Data mining
evidence indexing
examination and analysis
Indexing
keyword searching
Methodology
Mobile cloud forensics
Mobile computing
Optimization
Particle swarm optimization
Performance indices
PSO
Searching
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Summary: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.
ISSN:1939-3555
1939-3547
DOI:10.1080/19393555.2020.1839605