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1. |
Record Nr. |
UNINA9910299287503321 |
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Autore |
Quick Darren |
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Titolo |
Big Digital Forensic Data [[electronic resource] ] : Volume 1: Data Reduction Framework and Selective Imaging / / by Darren Quick, Kim-Kwang Raymond Choo |
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Pubbl/distr/stampa |
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Singapore : , : Springer Singapore : , : Imprint : Springer, , 2018 |
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ISBN |
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Edizione |
[1st ed. 2018.] |
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Descrizione fisica |
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1 online resource (109 pages) |
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Collana |
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SpringerBriefs on Cyber Security Systems and Networks, , 2522-5561 |
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Disciplina |
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Soggetti |
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Computer security |
Application software |
Forensic science |
Computers |
Law and legislation |
Systems and Data Security |
Information Systems Applications (incl. Internet) |
Forensic Science |
Legal Aspects of Computing |
Computer Appl. in Social and Behavioral Sciences |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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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. |
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