Evidence Reconstruction in Digital Forensics using Fuzzy Methods: to Detect Misused Evidence - Couverture souple
Synopsis
With the rapid development of computer networks the daily work of all trades is more and more dependent on computers. As a result, high-tech crimes, commercial fraud and other phenomena involving computers occur more frequently. So people pay more and more attention to digital forensics. Digital forensics are concerned with the use of digital information (image or document file) as source of evidence in investigations and legal proceedings. This project focuses on the analysis part of digital forensics. Analysis concerned to reconstruct digital evidence and draw conclusions based on the evidence found. This project applies fuzzy c-means clustering based on swarm intelligence to digital forensics. The aim of clustering is to partition set of data into clusters. A drawback of clustering is that it easily traps into local minima. To overcome this problem, swarm intelligence is used. Swarm Intelligence is an evolutionary, nature inspired computer technique of global search strategy.
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Evidence Reconstruction in Digital Forensics using Fuzzy Methods to Detect Misused Evidence
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LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 3659677892
ISBN 13 : 9783659677892
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Evidence Reconstruction in Digital Forensics using Fuzzy Methods to Detect Misused Evidence
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ISBN 10 : 3659677892
ISBN 13 : 9783659677892
Neuf
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Evidence Reconstruction in Digital Forensics using Fuzzy Methods: to Detect Misused Evidence
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 3659677892
ISBN 13 : 9783659677892
Neuf
Couverture souple
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Evidence Reconstruction in Digital Forensics using Fuzzy Methods
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Evidence Reconstruction in Digital Forensics using Fuzzy Methods
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LAP LAMBERT Academic Publishing Jul 2020, 2020
ISBN 10 : 3659677892
ISBN 13 : 9783659677892
Neuf
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -With the rapid development of computer networks the daily work of all trades is more and more dependent on computers. As a result, high-tech crimes, commercial fraud and other phenomena involving computers occur more frequently. So people pay more and more attention to digital forensics. Digital forensics are concerned with the use of digital information (image or document file) as source of evidence in investigations and legal proceedings. This project focuses on the analysis part of digital forensics. Analysis concerned to reconstruct digital evidence and draw conclusions based on the evidence found. This project applies fuzzy c-means clustering based on swarm intelligence to digital forensics. The aim of clustering is to partition set of data into clusters. A drawback of clustering is that it easily traps into local minima. To overcome this problem, swarm intelligence is used. Swarm Intelligence is an evolutionary, nature inspired computer technique of global search strategy. 104 pp. Englisch. N° de réf. du vendeur 9783659677892
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Evidence Reconstruction in Digital Forensics using Fuzzy Methods
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ISBN 13 : 9783659677892
Neuf
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Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Bartere MahipMahip Bartere is currently working as an Assistant Professor in G.H.Raisoni College of Engineering & Management, Amravati. He has published 40 papers in different international journals.With the rapid development of . N° de réf. du vendeur 494405409
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Evidence Reconstruction in Digital Forensics using Fuzzy Methods
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LAP LAMBERT Academic Publishing Jul 2020, 2020
ISBN 10 : 3659677892
ISBN 13 : 9783659677892
Neuf
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -With the rapid development of computer networks the daily work of all trades is more and more dependent on computers. As a result, high-tech crimes, commercial fraud and other phenomena involving computers occur more frequently. So people pay more and more attention to digital forensics. Digital forensics are concerned with the use of digital information (image or document file) as source of evidence in investigations and legal proceedings. This project focuses on the analysis part of digital forensics. Analysis concerned to reconstruct digital evidence and draw conclusions based on the evidence found. This project applies fuzzy c-means clustering based on swarm intelligence to digital forensics. The aim of clustering is to partition set of data into clusters. A drawback of clustering is that it easily traps into local minima. To overcome this problem, swarm intelligence is used. Swarm Intelligence is an evolutionary, nature inspired computer technique of global search strategy.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 104 pp. Englisch. N° de réf. du vendeur 9783659677892
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Evidence Reconstruction in Digital Forensics using Fuzzy Methods : to Detect Misused Evidence
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 3659677892
ISBN 13 : 9783659677892
Neuf
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - With the rapid development of computer networks the daily work of all trades is more and more dependent on computers. As a result, high-tech crimes, commercial fraud and other phenomena involving computers occur more frequently. So people pay more and more attention to digital forensics. Digital forensics are concerned with the use of digital information (image or document file) as source of evidence in investigations and legal proceedings. This project focuses on the analysis part of digital forensics. Analysis concerned to reconstruct digital evidence and draw conclusions based on the evidence found. This project applies fuzzy c-means clustering based on swarm intelligence to digital forensics. The aim of clustering is to partition set of data into clusters. A drawback of clustering is that it easily traps into local minima. To overcome this problem, swarm intelligence is used. Swarm Intelligence is an evolutionary, nature inspired computer technique of global search strategy. N° de réf. du vendeur 9783659677892
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