Big digital forensic data. Volume 1, Data reduction framework and selective imaging /

Saved in:
Bibliographic Details
Author / Creator:Quick, Darren, author.
Imprint:Singapore : Springer, 2018.
Description:1 online resource
Language:English
Series:SpringerBriefs on cyber security systems and networks, 2522-5561
SpringerBriefs on cyber security systems and networks.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11654516
Hidden Bibliographic Details
Varying Form of Title:Data reduction framework and selective imaging
Other authors / contributors:Choo, Kim-Kwang Raymond, author.
ISBN:9789811077630
9811077630
9811077622
9789811077623
9789811077623
Digital file characteristics:text file PDF
Notes:Includes bibliographical references.
Online resource; title from PDF title page (SpringerLink, viewed May 1, 2018).
Summary:This book provides an in-depth understanding of big data challenges to digital forensic investigations, also known as big digital forensic data. It also develops the basis of using data mining in big forensic data analysis, including data reduction, knowledge management, intelligence, and data mining principles to achieve faster analysis in digital forensic investigations. By collecting and assembling a corpus of test data from a range of devices in the real world, it outlines a process of big data reduction, and evidence and intelligence extraction methods. Further, it includes the experimental results on vast volumes of real digital forensic data. The book is a valuable resource for digital forensic practitioners, researchers in big data, cyber threat hunting and intelligence, data mining and other related areas.
Other form:Printed edition: 9789811077623
Standard no.:10.1007/978-981-10-7763-0

MARC

LEADER 00000cam a2200000Ii 4500
001 11654516
005 20210625185324.2
006 m o d
007 cr cnu|||unuuu
008 180430s2018 si ob 000 0 eng d
016 7 |a 019093795  |2 Uk 
019 |a 1034550256  |a 1038458328  |a 1048182186  |a 1059244433  |a 1066649398  |a 1082337222  |a 1088971349  |a 1105192085  |a 1113291006 
020 |a 9789811077630  |q (electronic bk.) 
020 |a 9811077630  |q (electronic bk.) 
020 |a 9811077622 
020 |a 9789811077623 
020 |z 9789811077623  |q (print) 
024 7 |a 10.1007/978-981-10-7763-0  |2 doi 
035 |a (OCoLC)1032810150  |z (OCoLC)1034550256  |z (OCoLC)1038458328  |z (OCoLC)1048182186  |z (OCoLC)1059244433  |z (OCoLC)1066649398  |z (OCoLC)1082337222  |z (OCoLC)1088971349  |z (OCoLC)1105192085  |z (OCoLC)1113291006 
035 9 |a (OCLCCM-CC)1032810150 
037 |a com.springer.onix.9789811077630  |b Springer Nature 
040 |a N$T  |b eng  |e rda  |e pn  |c N$T  |d GW5XE  |d N$T  |d AZU  |d UWO  |d EBLCP  |d UPM  |d COO  |d UAB  |d OCLCF  |d MERER  |d OCLCQ  |d VT2  |d U3W  |d UKMGB  |d OCLCQ  |d LVT  |d CNCEN  |d WYU  |d OCLCQ  |d DKU  |d CAUOI  |d BRX  |d CEF  |d AU@  |d UKAHL  |d LQU  |d OCLCQ  |d ADU  |d OCLCQ 
049 |a MAIN 
050 4 |a HV8079.C65 
072 7 |a SOC  |x 004000  |2 bisacsh 
072 7 |a UR  |2 bicssc 
072 7 |a UTN  |2 bicssc 
072 7 |a UR  |2 thema 
072 7 |a UTN  |2 thema 
100 1 |a Quick, Darren,  |e author.  |0 http://id.loc.gov/authorities/names/n2013058828 
245 1 0 |a Big digital forensic data.  |n Volume 1,  |p Data reduction framework and selective imaging /  |c Darren Quick, Kim-Kwang Raymond Choo. 
246 3 0 |a Data reduction framework and selective imaging 
264 1 |a Singapore :  |b Springer,  |c 2018. 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
490 1 |a SpringerBriefs on cyber security systems and networks,  |x 2522-5561 
504 |a Includes bibliographical references. 
588 0 |a Online resource; title from PDF title page (SpringerLink, viewed May 1, 2018). 
505 0 |a Chapter 1 Introduction -- Chapter 2 Background and Literature Review -- Chapter 3 Data Reduction and Data Mining Framework -- Chapter 4 Digital Forensic Data Reduction by Selective Imaging -- Chapter 5 Summary of the Framework and DRbSI. 
520 |a This book provides an in-depth understanding of big data challenges to digital forensic investigations, also known as big digital forensic data. It also develops the basis of using data mining in big forensic data analysis, including data reduction, knowledge management, intelligence, and data mining principles to achieve faster analysis in digital forensic investigations. By collecting and assembling a corpus of test data from a range of devices in the real world, it outlines a process of big data reduction, and evidence and intelligence extraction methods. Further, it includes the experimental results on vast volumes of real digital forensic data. The book is a valuable resource for digital forensic practitioners, researchers in big data, cyber threat hunting and intelligence, data mining and other related areas. 
650 0 |a Computer crimes  |x Investigation.  |0 http://id.loc.gov/authorities/subjects/sh85029493 
650 0 |a Big data.  |0 http://id.loc.gov/authorities/subjects/sh2012003227 
650 7 |a SOCIAL SCIENCE  |x Criminology.  |2 bisacsh 
650 7 |a Information retrieval.  |2 bicssc 
650 7 |a Forensic science.  |2 bicssc 
650 7 |a Legal aspects of IT.  |2 bicssc 
650 7 |a Society & social sciences.  |2 bicssc 
650 7 |a Computer security.  |2 bicssc 
650 7 |a Big data.  |2 fast  |0 (OCoLC)fst01892965 
650 7 |a Computer crimes  |x Investigation.  |2 fast  |0 (OCoLC)fst00872065 
655 4 |a Electronic books. 
700 1 |a Choo, Kim-Kwang Raymond,  |e author.  |0 http://id.loc.gov/authorities/names/n2008624679 
776 0 8 |i Printed edition:  |z 9789811077623 
830 0 |a SpringerBriefs on cyber security systems and networks. 
903 |a HeVa 
929 |a oclccm 
999 f f |i 4808ec0a-493b-5122-a3b9-8de44a495a76  |s 22cb64f1-d595-57af-ad87-0abd08e01e61 
928 |t Library of Congress classification  |a HV8079.C65  |l Online  |c UC-FullText  |u https://link.springer.com/10.1007/978-981-10-7763-0  |z Springer Nature  |g ebooks  |i 12553140