Machine learning and security : protecting systems with data and algorithms / Clarence Chio and David Freeman
Tipo de material: TextoDetalles de publicación: Sebastopol O'Reilly, 2018 Descripción: XV, 365 p. ; 25 cmISBN: 9781491979907Tema(s): Seguridad informática | Redes neuronales (Informática) | Algoritmos computacionales | Inteligencia artificialResumen: We wrote this book to provide a framework for discussing the inevitable marriage of two ubiquitous concepts: machine learning and security. While there is some literature on the intersection of these subjects (and multiple conference workshops: CCSâs AISec, AAAIâs AICS, and NIPSâs Machine Deception), most of the existing work is academic or theoretical. In particular, we did not find a guide that provides concrete, worked examples with code that can educate security practitioners about data science and help machine learning practitioners think about modern security problems effectively. In examining a broad range of topics in the security space, we provide examples of how machine learning can be applied to augment or replace rule-based or heuristic solutions to problems like intrusion detection, malware classification, or network analysis. In addition to exploring the core machine learning algorithms and techniques, we focus on the challenges of building maintainable, reliable, and scalable data mining systems in the security space. Through worked examples and guided discussions, we show you how to think about data in an adversarial environment and how to identify the important signals that can get drowned out by noise.Tipo de ítem | Biblioteca de origen | Signatura | Estado | Fecha de vencimiento | Código de barras | Reserva de ítems |
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Monografías | 03. BIBLIOTECA INGENIERÍA PUERTO REAL | 681.3.07-7/CHI/mac (Navegar estantería(Abre debajo)) | Disponible Ubicación en estantería | Bibliomaps® | 3744567419 |
Bibliografía
We wrote this book to provide a framework for discussing the inevitable marriage of two ubiquitous concepts: machine learning and security. While there is some literature on the intersection of these subjects (and multiple conference workshops: CCSâs AISec, AAAIâs AICS, and NIPSâs Machine Deception), most of the existing work is academic or theoretical. In particular, we did not find a guide that provides concrete, worked examples with code that can educate security practitioners about data science and help machine learning practitioners think about modern security problems effectively. In examining a broad range of topics in the security space, we provide examples of how machine learning can be applied to augment or replace rule-based or heuristic solutions to problems like intrusion detection, malware classification, or network analysis. In addition to exploring the core machine learning algorithms and techniques, we focus on the challenges of building maintainable, reliable, and scalable data mining systems in the security space. Through worked examples and guided discussions, we show you how to think about data in an adversarial environment and how to identify the important signals that can get drowned out by noise.
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