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Published by Information Science Reference, 2021
ISBN 10: 1799877906 ISBN 13: 9781799877905
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Comprehensively covers data-driven malware security with an emphasis on using statistical, machine learning and AI, as well as the current trends in ML/statistical approaches to detecting, clustering, and classification of cyber-threats.Klappentext.
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ISBN 10: 1799877906 ISBN 13: 9781799877905
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Published by Information Science Reference, 2021
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Published by Information Science Reference, 2021
ISBN 10: 1799877906 ISBN 13: 9781799877905
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Taschenbuch. Condition: Neu. Advances in Malware and Data-Driven Network Security | Brij B. Gupta | Taschenbuch | Kartoniert / Broschiert | Englisch | 2021 | Information Science Reference | EAN 9781799877905 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Language: English
Published by Information Science Reference, 2021
ISBN 10: 1799877892 ISBN 13: 9781799877899
Seller: moluna, Greven, Germany
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Comprehensively covers data-driven malware security with an emphasis on using statistical, machine learning and AI, as well as the current trends in ML/statistical approaches to detecting, clustering, and classification of cyber-threats.Klappentext.
Language: English
Published by Information Science Reference, 2021
ISBN 10: 1799877906 ISBN 13: 9781799877905
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Every day approximately three-hundred thousand to four-hundred thousand new malware are registered, many of them being adware and variants of previously known malware. Anti-virus companies and researchers cannot deal with such a deluge of malware - to analyze and build patches. The only way to scale the efforts is to build algorithms to enable machines to analyze malware and classify and cluster them to such a level of granularity that it will enable humans (or machines) to gain critical insights about them and build solutions that are specific enough to detect and thwart existing malware and generic-enough to thwart future variants. Advances in Malware and Data-Driven Network Security comprehensively covers data-driven malware security with an emphasis on using statistical, machine learning, and AI as well as the current trends in ML/statistical approaches to detecting, clustering, and classification of cyber-threats. Providing information on advances in malware and data-driven network security as well as future research directions, it is ideal for graduate students, academicians, faculty members, scientists, software developers, security analysts, computer engineers, programmers, IT specialists, and researchers who are seeking to learn and carry out research in the area of malware and data-driven network security.
Language: English
Published by Information Science Reference, 2021
ISBN 10: 1799877892 ISBN 13: 9781799877899
Seller: preigu, Osnabrück, Germany
Buch. Condition: Neu. Advances in Malware and Data-Driven Network Security | Brij B. Gupta | Buch | Gebunden | Englisch | 2021 | Information Science Reference | EAN 9781799877899 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Language: English
Published by Information Science Reference, 2021
ISBN 10: 1799877892 ISBN 13: 9781799877899
Seller: AHA-BUCH GmbH, Einbeck, Germany
Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Every day approximately three-hundred thousand to four-hundred thousand new malware are registered, many of them being adware and variants of previously known malware. Anti-virus companies and researchers cannot deal with such a deluge of malware - to analyze and build patches. The only way to scale the efforts is to build algorithms to enable machines to analyze malware and classify and cluster them to such a level of granularity that it will enable humans (or machines) to gain critical insights about them and build solutions that are specific enough to detect and thwart existing malware and generic-enough to thwart future variants. Advances in Malware and Data-Driven Network Security comprehensively covers data-driven malware security with an emphasis on using statistical, machine learning, and AI as well as the current trends in ML/statistical approaches to detecting, clustering, and classification of cyber-threats. Providing information on advances in malware and data-driven network security as well as future research directions, it is ideal for graduate students, academicians, faculty members, scientists, software developers, security analysts, computer engineers, programmers, IT specialists, and researchers who are seeking to learn and carry out research in the area of malware and data-driven network security.