Forensics is an important topic for law enforcement, civil litigators, corporate investigators, academics, and other professionals who deal with complex digital investigations. Digital forensics has played a major role in some of the largest criminal and civil investigations of the past two decades—most notably, the Enron investigation in the early 2000s. Forensics has been used in many different situations. From criminal cases, to civil litigation, to organization-initiated internal investigations, digital forensics is the way data becomes evidence—sometimes, the most important evidence—and that evidence is how many types of modern investigations are solved.
The increased usage of Big Data solutions, such as Hadoop, has required new approaches to how forensics is conducted, and with the rise in popularity of Big Data across a wide number of organizations, forensic investigators need to understand how to work with these solutions. The number of organizations who have implemented Big Data solutions has surged in the past decade. These systems house critical information that can provide information on an organization's operations and strategies—key areas of interest in different types of investigations. Hadoop has been the most popular of the Big Data solutions, and with its distributed architecture, in-memory data storage, and voluminous data storage capabilities, performing forensics on Hadoop offers new challenges to forensic investigators.
A new area within forensics, called Big Data forensics, focuses on the forensics of Big Data systems. These systems are unique in their scale, how they store data, and the practical limitations that can prevent an investigator from using traditional forensic means. The field of digital forensics has expanded from primarily dealing with desktop computers and servers to include mobile devices, tablets, and large-scale data systems. Forensic investigators have kept pace with the changes in technologies by utilizing new techniques, software, and hardware to collect, preserve, and analyze digital evidence. Big Data solutions, likewise, require different approaches to analyze the collected data.
In this book, the processes, tools, and techniques for performing a forensic investigation of Hadoop are described and explored in detail. Many of the concepts covered in this book can be applied to other Big Data systems—not just Hadoop. The processes for identifying and collecting forensic evidence are covered, and the processes for analyzing the data as part of an investigation and presenting the findings are detailed. Practical examples are given by using LightHadoop and Amazon Web Services to develop test Hadoop environments and perform forensics against them. By the end of the book, you will be able to work with the Hadoop command line and forensic software packages and understand the forensic process.
Chapter 1, Starting Out with Forensic Investigations and Big Data, is an overview of both forensics and Big Data. This chapter covers why Big Data is important, how it is being used, and how forensics of Big Data is different from traditional forensics.
Chapter 2, Understanding Hadoop Internals and Architecture, is a detailed explanation of Hadoop's internals and how data is stored within a Hadoop environment.
Chapter 3, Identifying Big Data Evidence, covers the process for identifying relevant data within Hadoop using techniques such as interviews, data sampling, and system reviews.
Chapter 4, Collecting Hadoop Distributed File System Data, details how to collect forensic evidence from the Hadoop Distributed File System (HDFS) using physical and logical collection methods.
Chapter 5, Collecting Hadoop Application Data, examines the processes for collecting evidence from Hadoop applications using logical- and query-based methods. HBase, Hive, and Pig are covered in this chapter.
Chapter 6, Performing Hadoop Distributed File System Analysis, details how to conduct a forensic analysis of HDFS evidence, utilizing techniques such as file carving and keyword analysis.
Chapter 7, Analyzing Hadoop Application Data, covers how to conduct a forensic analysis of Hadoop application data using databases and statistical analysis techniques. Topics such as Benford's law and clustering are discussed in this chapter.
Chapter 8, Presenting Forensic Findings, shows to how to present forensic findings for internal investigations or legal proceedings.
You need to have a basic understanding of the Linux command line and some experience working with a SQL DBMS. The exercises and examples in this book are presented in Amazon Web Services and LightHadoop—a Hadoop virtual machine distribution that is available for Oracle's VirtualBox, a free, cross-platform virtual machine software. Several forensic analysis tool examples are shown in Microsoft Windows, but they are also available for most Linux builds.
This book is for those who are interested in digital forensics and Hadoop. Written for readers who are new to both forensics and Big Data, most concepts are presented in a simplified, high-level manner. This book is intended as a getting-started guide in this area of forensics.
In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows:
"The following command collects the /dev/sda1 volume, stores it in a file called sda1.img".
A block of code is set as follows:
hdfs dfs -put ./testFile.txt /home/hadoopFile.txt hdfs dfs –get /home/hadoopFile.txt ./testFile_copy.txt md5sum testFile.txt md5sum testFile_copy.txt
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
hdfs dfs -put ./testFile.txt /home/hadoopFile.txt
hdfs dfs –get /home/hadoopFile.txt ./testFile_copy.txt
md5sum testFile.txt
md5sum testFile_copy.txtAny command-line input or output is written as follows:
#!/bin/bash hive -e "show tables;" > hiveTables.txt for line in $(cat hiveTables.txt) ; do hive -hiveconf tablename=$line -f tableExport.hql > ${line}.txt done
New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "Enter the Case Number and Examiner information, and click Next."
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