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Database Forensic Investigation Process Models: A Review

Arafat Al-Dhaqm, Shukor Abd Razak, Siti Hajar Othman, Abdulalem Ali, Fuad A. Ghaleb, Arieff Salleh Rosman, Nurazmallail Marni

2020IEEE Access46 citationsDOIOpen Access PDF

Abstract

Database Forensic Investigation (DBFI) involves the identification, collection, preservation, reconstruction, analysis, and reporting of database incidents. However, it is a heterogeneous, complex, and ambiguous field due to the variety and multidimensional nature of database systems. A small number of DBFI process models have been proposed to solve specific database scenarios using different investigation processes, concepts, activities, and tasks as surveyed in this paper. Specifically, we reviewed 40 proposed DBFI process models for RDBMS in the literature to offer up-to-date and comprehensive background knowledge on existing DBFI process model research, their associated challenges, issues for newcomers, and potential solutions for addressing such issues. This paper highlights three common limitations of the DBFI domain, which are: 1) redundant and irrelevant investigation processes; 2) redundant and irrelevant investigation concepts and terminologies; and 3) a lack of unified models to manage, share, and reuse DBFI knowledge. Also, this paper suggests three solutions for the discovered limitations, which are: 1) propose generic DBFI process/model for the DBFI field; 2) develop a semantic metamodeling language to structure, manage, organize, share, and reuse DBFI knowledge; and 3) develop a repository to store and retrieve DBFI field knowledge.

Topics & Concepts

Computer scienceProcess (computing)Field (mathematics)DatabaseReuseIdentification (biology)Data scienceDomain (mathematical analysis)Relational database management systemVariety (cybernetics)MetamodelingRelational databaseSoftware engineeringArtificial intelligencePure mathematicsOperating systemBotanyMathematical analysisEcologyMathematicsBiologyDigital and Cyber ForensicsData Quality and ManagementAdvanced Malware Detection Techniques
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