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E-raamat: Big Digital Forensic Data: Volume 1: Data Reduction Framework and Selective Imaging

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This book provides an in-depth understanding of big data challenges to digital forensic investigations, also known as big digital forensic data. It also develops the basis of using data mining in big forensic data analysis, including data reduction, knowledge management, intelligence, and data mining principles to achieve faster analysis in digital forensic investigations. By collecting and assembling a corpus of test data from a range of devices in the real world, it outlines a process of big data reduction, and evidence and intelligence extraction methods. Further, it includes the experimental results on vast volumes of real digital forensic data. The book is a valuable resource for digital forensic practitioners, researchers in big data, cyber threat hunting and intelligence, data mining and other related areas.

1 Introduction
1(4)
References
4(1)
2 Background and Literature Review
5(42)
2.1 Background
6(1)
2.2 Volume of Data
7(5)
2.3 Growth of Media
12(4)
2.4 Processing Time
16(1)
2.5 Proposed Solutions
17(14)
2.5.1 Data Mining
18(4)
2.5.2 Data Reduction and Subsets
22(6)
2.5.3 Triage
28(3)
2.6 Other Proposed Solutions to the Data Volume Challenge
31(4)
2.7 Discussion
35(3)
2.8 Summary
38(9)
References
40(7)
3 Data Reduction and Data Mining Frame-Work
47(22)
3.1 Motivation
48(6)
3.2 Proposed Digital Forensic Data Reduction and Data Mining Framework
54(6)
3.3 Pilot Study Preliminary Findings
60(3)
3.4 Discussion
63(1)
3.5 Summary
64(5)
References
65(4)
4 Digital Forensic Data Reduction by Selective Imaging
69(24)
4.1 Digital Forensic Data Reduction by Selective Imaging
69(13)
4.1.1 Load or Mount Forensic Image or Connect Physical Device
76(1)
4.1.2 Processing Options
76(1)
4.1.3 Filters for Subset Files
77(1)
4.1.4 File System Filters
77(1)
4.1.5 Operating System Filters
77(1)
4.1.6 Software and Applications Filters
78(1)
4.1.7 User Data Filters
78(1)
4.1.8 Review of Large Files
79(1)
4.1.9 Excluded File Types and Overwritten Files and Data
79(1)
4.1.10 File and Data List (Spreadsheet) Report
79(1)
4.1.11 Video Thumbnails Method
80(1)
4.1.12 Picture Dimension Reduction
81(1)
4.1.13 Preservation
81(1)
4.2 Test Data Application and Results
82(1)
4.3 Application to Real World Digital Forensic Case Data
83(5)
4.3.1 Real World Data Subset Reduction
83(2)
4.3.2 Real World Data---Video Thumbnailing
85(1)
4.3.3 Real World Data---Case Examples and Post Case Analysis
85(3)
4.3.4 Real World Data---Cross Case Analysis
88(1)
4.3.5 Real World Data---Failing Hard Disk Drives
88(1)
4.4 Discussion
88(3)
4.5 Summary
91(2)
References
91(2)
5 Summary of the Framework and DRbSI
93
5.1 Conclusion
94
References
96
Dr. Darren Quick is a Senior Intelligence Technologist with the Australian Department of Home Affairs and a former Digital Forensic Investigator with the Australian Border Force, and previously an Electronic Evidence Specialist with the South Australia Police. He has undertaken over 650 digital forensic investigations involving many thousands of digital evidence items. In 2012 Darren was awarded membership of the Golden Key International Honour Society, in 2014 he received a Highly Commended award from the Australian National Institute of Forensic Science, and in 2015 received the Publication of the Year award from the Australian Institute of Professional Intelligence Officers.





Dr. Kim-Kwang Raymond Choo holds the Cloud Technology Endowed Professorship at The University of Texas at San Antonio, is an adjunct associate professor at the University of South Australia, a fellow of the Australian Computer Society, and a senior member of IEEE. He and his team wonthe Digital Forensics Research Challenge 2015 organized by Germany's University of Erlangen-Nuremberg, and he is the recipient of various awards including the ESORICS 2015 Best Paper Award, the 2014 Highly Commended Award from the Australia New Zealand Policing Advisory Agency, Fulbright Scholarship in 2009, the 2008 Australia Day Achievement Medallion, and the British Computer Society's Wilkes Award in 2008.