TY  - GEN
N2  - 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.
DO  - 10.1007/978-981-10-7763-0
DO  - doi
AB  - 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.
T1  - Big digital forensic data.
AU  - Quick, Darren,
AU  - Choo, Kim-Kwang Raymond,
CN  - HV8079.C65
ID  - 838803
KW  - Computer crimes
KW  - Big data.
SN  - 9789811077630
SN  - 9811077630
TI  - Big digital forensic data.
LK  - https://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-10-7763-0
UR  - https://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-10-7763-0
ER  -