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001 ocn880706315
003 OCoLC
005 20190328114807.0
006 m o d
007 cr cnu---unuuu
008 140530s2014 mau ob 001 0 eng d
040 _aOPELS
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019 _a881162175
_a883127972
020 _a9780124186835
_q(electronic bk.)
020 _a0124186831
_q(electronic bk.)
020 _a9781306820547
020 _a1306820545
020 _z0124186769
020 _z9780124186767
035 _a(OCoLC)880706315
_z(OCoLC)881162175
_z(OCoLC)883127972
050 4 _aQA76.73.P98
072 7 _aCOM
_x051360
_2bisacsh
082 0 4 _a005.13/3
_223
100 1 _aHosmer, Chet,
_eauthor.
245 1 0 _aPython forensics : a workbench for inventing and sharing digital forensic technology /
_h[electronic resource]
_cChet Hosmer.
264 1 _aWaltham, MA :
_bSyngress,
_c2014.
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
520 _aPython Forensics provides many never-before-published proven forensic modules, libraries, and solutions that can be used right out of the box. In addition, detailed instruction and documentation provided with the code samples will allow even novice Python programmers to add their own unique twists or use the models presented to build new solutions. Rapid development of new cybercrime investigation tools is an essential ingredient in virtually every case and environment. Whether you are performing post-mortem investigation, executing live triage, extracting evidence from mobile devices or cloud services, or you are collecting and processing evidence from a network, Python forensic implementations can fill in the gaps. Drawing upon years of practical experience and using numerous examples and illustrative code samples, author Chet Hosmer discusses how to: Develop new forensic solutions independent of large vendor software release schedules Participate in an open-source workbench that facilitates direct involvement in the design and implementation of new methods that augment or replace existing tools Advance your career by creating new solutions along with the construction of cutting-edge automation solutions to solve old problems.
500 _aIncludes index.
588 0 _aOnline resource; title from PDF title page (ScienceDirect, viewed May 30, 2014).
504 _aIncludes bibliographical references and index.
505 0 _aFront Cover; Python Forensics: A Workbench for Inventing and Sharing Digital Forensic Technology; Copyright; Dedication; Acknowledgments; Endorsements; Contents; List of figures; About the Author; About the Technical Editor; Foreword; Preface; Intended Audience; Prerequisites; Reading this Book; Supported Platforms; Download Software; Comments, Questions, and Contributions; Chapter 1: Why Python Forensics?; Introduction; Cybercrime investigation challenges; How can the Python programming environment help meet these challenges?; Global support for Python; Open source and platform independence.
505 8 _aLifecycle positioningCost and barriers to entry; Python and the Daubert evidence standard; Organization of the book; Chapter review; Summary questions; Additional Resources; Chapter 2: Setting up a Python Forensics Environment; Introduction; Setting up a python forensics environment; The right environment; The Python Shell; Choosing a python version; Installing python on windows; Python packages and modules; The Python Standard Library; What is included in the standard library?; Built-in functions; hex() and bin(); range(); Other built-in functions; Built-in constants; Built-in types.
505 8 _aBuilt-in exceptionsFile and directory access; Data compression and archiving; File formats; Cryptographic services; Operating system services; Standard Library summary; Third-party packages and modules; The natural language toolkit [NLTK]; Twisted matrix [TWISTED]; Integrated development environments; What are the options?; IDLE; WingIDE; Python running on Ubuntu Linux; Python on mobile devices; iOS Python app; Windows 8 phone; A virtual machine; Chapter review; Summary questions; Looking ahead; Additional Resources; Chapter 3: Our First Python Forensics App; Introduction.
505 8 _aNaming conventions and other considerationsConstants; Local variable name; Global variable name; Functions name; Object name; Module; Class names; Our first application ``one-way file system hashing� � ; Background; One-way hashing algorithms basic characteristics; Popular cryptographic hash algorithms?; What are the tradeoffs between one-way hashing algorithms?; What are the best-use cases for one-way hashing algorithms in forensics?; Fundamental requirements; Design considerations; Program structure; Main function; ParseCommandLine; WalkPath function; HashFile function; CSVWriter (class).
505 8 _aLoggerWriting the code; Code walk-through; Examining main-code walk-through; ParseCommandLine(); ValiditingDirectoryWritable; WalkPath; HashFile; CSVWriter; Full code listing pfish.py; Full code listing _pfish.py; Results presentation; Chapter review; Summary questions; Looking ahead; Additional Resources; Chapter 4: Forensic Searching and Indexing Using Python; Introduction; Keyword context search; How can this be accomplished easily in Python?; Fundamental requirements; Design considerations; Main function; ParseCommandLine; SearchWords function; PrintBuffer functions; logger.
650 0 _aPython (Computer program language)
650 7 _aCOMPUTERS
_xProgramming Languages
_xPython.
_2bisacsh
650 7 _aPython (Computer program language)
_2fast
_0(OCoLC)fst01084736
655 4 _aElectronic books.
776 0 8 _iPrint version:
_aHosmer, Chet.
_tPython forensics : a workbench for inventing and sharing digital forensic technology.
_dWaltham, Massachusetts : Syngress, �2014
_hxxviii, 318 pages
_z9780124186767
856 4 0 _3ScienceDirect
_uhttp://www.sciencedirect.com/science/book/9780124186767
999 _c246916
_d246916