Scaling bulk data analysis with mapreduce

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Authors
Andrzejewski, Timothy J.
Subjects
hadoop
mapreduce
digital forensics
bulk data analysis
bulk_extractor
distributed digital forensics
data mining
big data
Advisors
McCarrin, Michael
Stefanou, Marcus S.
Date of Issue
2017-09
Date
Sep-17
Publisher
Monterey, California: Naval Postgraduate School
Language
Abstract
Between 2005 and 2015, the world population grew by 11% while hard drive capacity grew by 95%. Increased demand for storage combined with decreasing costs presents challenges for digital forensic analysts working within tight time constraints. Advancements have been made to current tools to assist the analyst, but many require expensive specialized systems, knowledge and software. This thesis provides a method to address these challenges through distributed analysis of raw forensic images stored in a distributed file system using open-source software.We develop a proof-of-concept tool capable of counting unique bytes in a 116 TiB corpus of drives in 1 hour 41 minutes, demonstrating a peak throughput of 18.33 GiB/s on a 25-node Hadoop cluster. Furthermore, we demonstrate the ability to perform email address extraction on the corpus in 2 hours 5 minutes, for a throughput of 15.84 GiB/s, a result that compares favorably to traditional email address extraction methods, which we estimate to run with a throughput of approximately 91 MiB/s on a 24-core production server. Primary contributions to the forensic community are: 1) a distributed, scalable method to analyze large data sets in a practical timeframe, 2) a MapReduce program to count unique bytes of any forensic image, and 3) a MapReduce program capable of extracting 233 million email addresses from a 116 TiB corpus in just over two hours.
Type
Thesis
Description
Series/Report No
Department
Computer Science (CS)
Organization
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NPS Report Number
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Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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