Big forensic data management in heterogeneous distributed systems: quick analysis of multimedia forensic data. (22nd July 2016)

• Record Type: Journal Article
• Authors: Quick, Darren; Choo, Kim‐Kwang Raymond
• Other Names: Chen Dan guestEditor.; Wang Lizhe guestEditor.; Zhou Suiping guestEditor.
• Is Part Of: Software, practice & experience. Volume 47:Number 8(2017)
• Page Start: 1095
• Page End: 1109
• Publication Date: 2016-07-22
• Subjects: big forensic data management -- big forensic data reduction -- digital forensic resource management -- heterogeneous distributed system forensics -- smart city forensic readiness; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2429
• Languages: English
• ISSNs: 0038-0644

Abstract

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.