Research Catalog

Digital forensic science : issues, methods, and challenges / Vassil Roussev.

Title
Digital forensic science : issues, methods, and challenges / Vassil Roussev.
Author
Roussev, Vassil
Publication
  • [San Rafael, California] : Morgan & Claypool, [2017]
  • ©2017

Items in the Library & Off-site

Filter by

1 Item

StatusFormatAccessCall NumberItem Location
TextRequest in advance HV8073 .R683 2017Off-site

Holdings

Details

Description
xiii, 141 pages : illustrations; 24 cm
Summary
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.
Series Statement
Synthesis lectures on information security, privacy, and trust, 1945-9742 ; #19
Uniform Title
  • Synthesis digital library of engineering and computer science.
  • Synthesis lectures on information security, privacy and trust #19.
Subject
  • SOCIAL SCIENCE / General
  • BUSINESS & ECONOMICS / Infrastructure
  • Forensic sciences > Data processing
  • Computer crimes > Investigation
  • Electronics in criminal investigation
  • Data recovery (Computer science)
  • Forensic sciences > Data processing
  • digital forensics
  • cyber forensics
  • cyber crime
  • incident response
  • data recovery
Note
  • Part of: Synthesis digital library of engineering and computer science.
Bibliography (note)
  • Includes bibliographical references (pages 125-140).
Processing Action (note)
  • committed to retain
Contents
  • 1. Introduction -- 1.1 Scope of this book -- 1.2 Organization --
  • 2. Brief history -- 2.1 Early years (1984-1996) -- 2.2 Golden age (1997-2007) -- 2.3 Present (2007-) -- 2.4 Summary --
  • 3. Definitions and models -- 3.1 The Daubert standard -- 3.2 Digital forensic science definitions -- 3.2.1 Law-centric definitions -- 3.2.2 Working technical definition -- 3.3 Models of forensic analysis -- 3.3.1 Differential analysis -- 3.3.2 Computer history model -- 3.3.3 Cognitive task model --
  • 4. System analysis -- 4.1 Storage forensics -- 4.1.1 Data abstraction layers -- 4.1.2 Data acquisition -- 4.1.3 Forensic image formats -- 4.1.4 Filesystem analysis -- 4.1.5 Case study: FAT32 -- 4.1.6 Case study: NTFS -- 4.1.7 Data recovery and file content carving -- 4.1.8 File fragment classification -- 4.2 Main memory forensics -- 4.2.1 Memory acquisition -- 4.2.2 Memory image analysis -- 4.3 Network forensics -- 4.4 Real-time processing and triage -- 4.4.1 Real-time computing -- 4.4.2 Forensic computing with deadlines -- 4.4.3 Triage -- 4.5 Application forensics -- 4.5.1 Web browser -- 4.5.2 Cloud drives -- 4.6 Cloud forensics -- 4.6.1 Cloud basics -- 4.6.2 The cloud forensics landscape -- 4.6.3 IaaS forensics -- 4.6.4 SaaS forensics --
  • 5. Artifact analysis -- 5.1 Finding known objects: cryptographic hashing -- 5.2 Block-level analysis -- 5.3 Efficient hash representation: Bloom filters -- 5.4 Approximate matching -- 5.4.1 Content-defined data chunks -- 5.4.2 Ssdeep -- 5.4.3 Sdhash -- 5.4.4 Evaluation -- 5.5 Cloud-native artifacts --
  • 6. Open issues and challenges -- 6.1 Scalability -- 6.2 Visualization and collaboration -- 6.3 Automation and intelligence -- 6.4 Pervasive encryption -- 6.5 Cloud computing -- 6.5.1 From SaaP to SaaS -- 6.5.2 Separating cloud services from their implementation -- 6.5.3 Research challenges -- 6.6 Internet of things (IoT) -- Bibliography -- Author's biography.
ISBN
  • 9781627059596
  • 1627059598
OCLC
984515481
Owning Institutions
Harvard Library