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Da: Corner of a Foreign Field, Tokyo, TOKYO, Giappone
Prima edizione
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Aggiungi al carrelloSoft cover. Condizione: Good. No Jacket. 1st Edition. 2018.Softcover.Good condition.Some highlighting to the text.91 pages.Ships from Japan.Usually ships in 1-2 working days.
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Condizione: New. pp. 91.
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Lingua: Inglese
Editore: Springer-Verlag New York Inc, 2018
ISBN 10: 3319737872 ISBN 13: 9783319737874
Da: Revaluation Books, Exeter, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: Brand New. pap/psc edition. 100 pages. 9.25x6.10x0.28 inches. In Stock.
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Lingua: Inglese
Editore: Springer International Publishing, 2018
ISBN 10: 3319737872 ISBN 13: 9783319737874
Da: moluna, Greven, Germania
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Lingua: Inglese
Editore: Springer, Berlin, Springer, 2018
ISBN 10: 3319737872 ISBN 13: 9783319737874
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This SpringerBrief discusses how to develop intelligent systems for cyber attribution regarding cyber-attacks. Specifically, the authors review the multiple facets of the cyber attribution problem that make it difficult for 'out-of-the-box' artificial intelligence and machine learning techniques to handle. Attributing a cyber-operation through the use of multiple pieces of technical evidence (i.e., malware reverse-engineering and source tracking) and conventional intelligence sources (i.e., human or signals intelligence) is a difficult problem not only due to the effort required to obtain evidence, but the ease with which an adversary can plant false evidence. This SpringerBrief not only lays out the theoretical foundations for how to handle the unique aspects of cyber attribution - and how to update models used for this purpose - but it also describes a series of empirical results, as well as compares results of specially-designed frameworks for cyber attribution to standard machine learning approaches. Cyber attribution is not only a challenging problem, but there are also problems in performing such research, particularly in obtaining relevant data. This SpringerBrief describes how to use capture-the-flag for such research, and describes issues from organizing such data to running your own capture-the-flag specifically designed for cyber attribution. Datasets and software are also available on the companion website.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Artificial Intelligence Tools for Cyber Attribution | Eric Nunes (u. a.) | Taschenbuch | viii | Englisch | 2018 | Springer | EAN 9783319737874 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Da: Brook Bookstore On Demand, Napoli, NA, Italia
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Aggiungi al carrelloCondizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Berlin Springer International Publishing Springer Feb 2018, 2018
ISBN 10: 3319737872 ISBN 13: 9783319737874
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This SpringerBrief discusses how to develop intelligent systems for cyber attribution regarding cyber-attacks. Specifically, the authors review the multiple facets of the cyber attribution problem that make it difficult for 'out-of-the-box' artificial intelligence and machine learning techniques to handle. Attributing a cyber-operation through the use of multiple pieces of technical evidence (i.e., malware reverse-engineering and source tracking) and conventional intelligence sources (i.e., human or signals intelligence) is a difficult problem not only due to the effort required to obtain evidence, but the ease with which an adversary can plant false evidence. This SpringerBrief not only lays out the theoretical foundations for how to handle the unique aspects of cyber attribution - and how to update models used for this purpose - but it also describes a series of empirical results, as well as compares results of specially-designed frameworks for cyber attribution to standard machine learning approaches. Cyber attribution is not only a challenging problem, but there are also problems in performing such research, particularly in obtaining relevant data. This SpringerBrief describes how to use capture-the-flag for such research, and describes issues from organizing such data to running your own capture-the-flag specifically designed for cyber attribution. Datasets and software are also available on the companion website. 91 pp. Englisch.
Da: Majestic Books, Hounslow, Regno Unito
EUR 75,56
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Aggiungi al carrelloCondizione: New. Print on Demand pp. 91.
Da: Biblios, Frankfurt am main, HESSE, Germania
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND pp. 91.