Muutke küpsiste eelistusi

Digital Forensic Science: Issues, Methods, and Challenges [Pehme köide]

Series edited by , Series edited by ,
Teised raamatud teemal:
Teised raamatud teemal:

Digital forensic science, or digital forensics, is the application of scientific tools and methods to identify, collect, and analyze digital (data) artifacts in support of legal proceedings. From a more technical perspective, it is the process of reconstructing the relevant sequence of events that have led to the currently observable state of a target IT system or (digital) artifacts.

Over the last three decades, the importance of digital evidence has grown in lockstep with the fast societal adoption of information technology, which has resulted in the continuous accumulation of data at an exponential rate. Simultaneously, there has been a rapid growth in network connectivity and the complexity of IT systems, leading to more complex behavior that needs to be investigated.

The goal of this book is to provide a systematic technical overview of digital forensic techniques, primarily from the point of view of computer science. This allows us to put the field in the broader perspective of a host of related areas and gain better insight into the computational challenges facing forensics, as well as draw inspiration for addressing them. This is needed as some of the challenges faced by digital forensics, such as cloud computing, require qualitatively different approaches; the sheer volume of data to be examined also requires new means of processing it.

1 Introduction
1(4)
1.1 Scope of this Book
1(1)
1.2 Organization
2(3)
2 Brief History
5(4)
2.1 Early Years (1984--1996)
5(1)
2.2 Golden Age (1997--2007)
6(1)
2.3 Present (2007--)
7(1)
2.4 Summary
8(1)
3 Definitions and Models
9(20)
3.1 The Daubert Standard
9(1)
3.2 Digial Forensic Science Definitions
10(2)
3.2.1 Law-centric Definitions
10(1)
3.2.2 Working Technical Definition
11(1)
3.3 Models of Forensic Analysis
12(17)
3.3.1 Differential Analysis
12(4)
3.3.2 Computer History Model
16(7)
3.3.3 Cognitive Task Model
23(6)
4 System Analysis
29(70)
4.1 Storage Forensics
29(33)
4.1.1 Data Abstraction Layers
29(2)
4.1.2 Data Acquisition
31(2)
4.1.3 Forensic Image Formats
33(6)
4.1.4 Filesystem Analysis
39(9)
4.1.5 Case Study: FAT32
48(1)
4.1.6 Case Study: NTFS
49(3)
4.1.7 Data Recovery and File Content Carving
52(5)
4.1.8 File Fragment Classification
57(5)
4.2 Main Memory Forensics
62(8)
4.2.1 Memory Acquisition
63(4)
4.2.2 Memory Image Analysis
67(3)
4.3 Network Forensics
70(3)
4.4 Real-time Processing and Triage
73(6)
4.4.1 Real-time Computing
74(1)
4.4.2 Forensic Computing with Deadlines
74(3)
4.4.3 Triage
77(2)
4.5 Application Forensics
79(3)
4.5.1 Web Browser
80(1)
4.5.2 Cloud Drives
81(1)
4.6 Cloud Forensics
82(17)
4.6.1 Cloud Basics
82(2)
4.6.2 The Cloud Forensics Landscape
84(2)
4.6.3 IaaS Forensics
86(3)
4.6.4 SaaS Forensics
89(10)
5 Artifact Analysis
99(18)
5.1 Finding Known Objects: Cryptographic Hashing
99(1)
5.2 Block-level Analysis
100(1)
5.3 Efficient Hash Representation: Bloom Filters
101(1)
5.4 Approximate Matching
102(8)
5.4.1 Content-defined Data Chunks
104(1)
5.4.2 Ssdeep
105(2)
5.4.3 Sdhash
107(2)
5.4.4 Evaluation
109(1)
5.5 Cloud-native Artifacts
110(7)
6 Open Issues and Challenges
117(8)
6.1 Scalability
117(1)
6.2 Visualization and Collaboration
117(1)
6.3 Automation and Intelligence
118(1)
6.4 Pervasive Encryption
118(1)
6.5 Cloud Computing
119(3)
6.5.1 From SaaP to SaaS
119(1)
6.5.2 Separating Cloud Services from their Implementation
120(1)
6.5.3 Research Challenges
121(1)
6.6 Internet of Things (IoT)
122(3)
Bibliography 125(16)
Author's Biography 141