Machine Learning and Security | Protecting Systems with Data and Algorithms
Idioma: inglés
Editorial: O'Reilly Media, 2018
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Machine Learning and Security | Protecting Systems with Data and Algorithms | Clarence Chio (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2018 | O'Reilly Media | EAN 9781491979907 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
N° de ref. del artículo 108788144
- Título
- Machine Learning and Security | Protecting Systems with Data and Algorithms
- Autor
- Clarence Chio (u. a.)
- Editorial
- O'Reilly Media
- Año de publicación
- 2018
- Estado
- Neu
- Encuadernación
- Taschenbuch
- Idioma
- inglés
- ISBN 10
- 1491979909
- ISBN 13
- 9781491979907
- Peso del artículo
- 676 gramos
- Dimensiones
- 231 x 177 x 25 mm
- Catálogos de vendedores
- Bücher
Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you'll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis.
Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike.
- Learn how machine learning has contributed to the success of modern spam filters
- Quickly detect anomalies, including breaches, fraud, and impending system failure
- Conduct malware analysis by extracting useful information from computer binaries
- Uncover attackers within the network by finding patterns inside datasets
- Examine how attackers exploit consumer-facing websites and app functionality
- Translate your machine learning algorithms from the lab to production
- Understand the threat attackers pose to machine learning solutions
“Sinopsis” puede pertenecer a otra edición de este título.
Acerca del autor
David Freeman is a research scientist/engineer at Facebook working on spam and abuse problems. He previously led anti-abuse engineering and data science teams at LinkedIn, where he built statistical models to detect fraud and abuse and worked with the larger machine learning community at LinkedIn to build scalable modeling and scoring infrastructure. He is an author, presenter, and organizer at international conferences on machine learning and security, such as NDSS, WWW, and AISec, and has published more than twenty academic papers on mathematical and statistical aspects of computer security. He holds a PhD in mathematics from UC Berkeley and did postdoctoral research in cryptography and security at CWI and Stanford University.
“Acerca de” puede pertenecer a otra edición de este título.
preigu
Osnabrück, Alemania
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