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100%: Paulo Shakarian; Eric Nunes; Andrew Ruef; Gerardo Simari: Artificial Intelligence Tools for Cyber Attribution (ISBN: 9783319737881) Springer Nature, in Deutsch, auch als eBook.Nur diese Ausgabe anzeigen…
89%: Paulo Shakarian, Eric Nunes, Andrew Ruef, Gerardo Simari: Artificial Intelligence Tools for Cyber Attribution (SpringerBriefs in Computer Science) (ISBN: 9783319737874) 2018, Springer Shop, Erstausgabe, in Englisch, Broschiert.Nur diese Ausgabe anzeigen…
Artificial Intelligence Tools for Cyber Attribution
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Bester Preis: € 33,34 (vom 01.01.2018)1
Artificial Intelligence Tools for Cyber Attribution
~EN NWISBN: 9783319737874 bzw. 3319737872, vermutlich in Englisch, neu.
Lieferung aus: Vereinigtes Königreich Großbritannien und Nordirland, Lieferzeit: 11 Tage, zzgl. Versandkosten.
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.
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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Artificial Intelligence Tools for Cyber Attribution
~EN PB NWISBN: 9783319737874 bzw. 3319737872, vermutlich in Englisch, Springer Shop, Taschenbuch, neu.
Lieferung aus: Mexiko, Lagernd, zzgl. Versandkosten.
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. Soft cover.
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. Soft cover.
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Gebr. - Artificial Intelligence Tools for Cyber Attribution (SpringerBriefs in Computer Science) (2018)
~EN PB NWISBN: 9783319737874 bzw. 3319737872, vermutlich in Englisch, Taschenbuch, neu.
Lieferung aus: Deutschland, 01-3 Tage.
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Artificial Intelligence Tools for Cyber Attribution (SpringerBriefs in Computer Science) (2018)
EN HC NW FEISBN: 9783319737874 bzw. 3319737872, in Englisch, 122 Seiten, Springer, gebundenes Buch, neu, Erstausgabe.
Lieferung aus: Deutschland, Noch nicht erschienen. Versandkostenfrei.
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Artificial Intelligence Tools for Cyber Attribution
DE NW EBISBN: 9783319737881 bzw. 3319737880, in Deutsch, Springer Nature, neu, E-Book.
Lieferung aus: Vereinigte Staaten von Amerika, Lagernd.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
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Artificial Intelligence Tools (2018)
~DE PB NWISBN: 9783319737874 bzw. 3319737872, vermutlich in Deutsch, Taschenbuch, neu.
Lieferung aus: Deutschland, Next Day, Versandkostenfrei.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
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