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Big forensic data management in heterogeneous distributed systems: quick analysis of multimedia forensic data
Author(s) -
Quick Darren,
Choo KimKwang Raymond
Publication year - 2017
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.2429
Subject(s) - computer science , digital forensics , cloud computing , process (computing) , data science , digital evidence , big data , volume (thermodynamics) , digital data , the internet , intelligence analysis , data collection , reduction (mathematics) , computer security , multimedia , data mining , world wide web , data transmission , computer network , physics , quantum mechanics , statistics , geometry , mathematics , operating system
Summary The growth in the data volume and number of evidential data from heterogeneous distributed systems in smart cities, such as cloud and fog computing systems and Internet‐of‐Things devices (e.g. IP‐based CCTVs), has led to increased collection, processing and analysis times, potentially resulting in vulnerable persons (e.g. victims of terrorism incidents) being at risk. A process of Digital Forensic Data Reduction of source multimedia and forensic images has provided a method to reduce the collection time and volume of data. In this paper, a methodology of Digital Forensic Quick Analysis is outlined, which describes a method to review Digital Forensic Data Reduction subsets to pinpoint relevant evidence and intelligence from heterogeneous distributed systems in a timely manner. Applying the proposed methodology to real‐world data from an Australian police agency highlighted the timeliness of the process, resulting in significant improvements in processing times in comparison with processing a full forensic image. The Quick Analysis methodology, combined with Digital Forensic Data Reduction, has potential to locate evidence and intelligence in a timely manner. Copyright © 2016 John Wiley & Sons, Ltd.