Machine learning in cyber trust: security, privacy, and reliability

Many networked computer systems are far too vulnerable to cyber attacks that can inhibit their functioning, corrupt important data, or expose private information. Not surprisingly, the field of cyber-based systems is a fertile groundwhere many tasks can be formulated as learning problems and approached in terms of machine learning algorithms. This book contains original materials by leading researchers in the area and covers applications of different machine learning methods in the reliability, security, performance, and privacy issues ofcyber space. It enables readers to discover what types of learning methods are at their disposal, summarizing the state-of-the-practice in this significantarea, and giving a classification of existing work. Those working in the field of cyber-based systems, including industrial managers, researchers, engineers, and graduate and senior undergraduate students will find this an indispensable guide in creating systems resistant to and tolerant of cyber attacks. Provides the reader with an overview of machine learning methods Demonstrates how machine learning is used to deal with the security, reliability, performance, and privacy of cyber-based systems Presents the state-of-the-practice in machine learning and cyber systems and identifies further efforts needed to producefruitful results INDICE: Introduction.- Cyber terrorism.- Machine learning.- Security.- Reliability.- Privacy.- Intrusion detection.- Web security.- Conclusion.- Reference.

• ISBN: 978-0-387-88734-0
• Editorial: Springer
• Encuadernacion: Cartoné
• Páginas: 325
• Fecha Publicación: 01/04/2009
• Nº Volúmenes: 1
• Idioma: Inglés