Machine Learning for Cybersecurity: Innovative Deep Learning Solutions (SpringerBriefs in Computer Science) - Tapa blanda

Omar, Marwan

 
9783031158926: Machine Learning for Cybersecurity: Innovative Deep Learning Solutions (SpringerBriefs in Computer Science)
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ISBN 10: 303115892X ISBN 13: 9783031158926
Editorial: Springer, 2022

Sinopsis

This SpringerBrief presents the underlying principles of machine learning and how to deploy various deep learning tools and techniques to tackle and solve certain challenges facing the cybersecurity industry.

By implementing innovative deep learning solutions, cybersecurity researchers, students and practitioners can analyze patterns and learn how to prevent cyber-attacks and respond to changing malware behavior. 

The knowledge and tools introduced in this brief can also assist cybersecurity teams to become more proactive in preventing threats and responding to active attacks in real time. It can reduce the amount of time spent on routine tasks and enable organizations to use their resources more strategically. In short, the knowledge and techniques provided in this brief can help make cybersecurity simpler, more proactive, less expensive and far more effective

Advanced-level students in computer science studying machine learning with a cybersecurity focus will find this SpringerBrief useful as a study guide. Researchers and cybersecurity professionals focusing on the application of machine learning tools and techniques to the cybersecurity domain will also want to purchase this SpringerBrief.

"Sinopsis" puede pertenecer a otra edición de este libro.

Acerca del autor

​Dr. Marwan Omar is an Associate Professor of Cybersecurity at Illinois Institute of Technology since August, 2022.  Dr. Omar received a Master’s degree in Information Systems and Technology from the University of Phoenix, 2009 and a Doctorate Degree in Digital Systems Security from Colorado Technical University, 2012. Dr. Omar has a track record of publications in the area of cyber security along with extensive teaching experience as well as industry experience. Dr. Omar recently earned a Post-Doctoral certificate from the University of Fernando Pessoa, Portugal and holds numerous industry certifications including CEH, Sec+, GASF, and CDPSE, among others.

De la contraportada

This SpringerBrief presents the underlying principles of machine learning and how to deploy various deep learning tools and techniques to tackle and solve certain challenges facing the cybersecurity industry.

By implementing innovative deep learning solutions, cybersecurity researchers, students and practitioners can analyze patterns and learn how to prevent cyber-attacks and respond to changing malware behavior. 

The knowledge and tools introduced in this brief can also assist cybersecurity teams to become more proactive in preventing threats and responding to active attacks in real time. It can reduce the amount of time spent on routine tasks and enable organizations to use their resources more strategically. In short, the knowledge and techniques provided in this brief can help make cybersecurity simpler, more proactive, less expensive and far more effective

Advanced-level students in computerscience studying machine learning with a cybersecurity focus will find this SpringerBrief useful as a study guide. Researchers and cybersecurity professionals focusing on the application of machine learning tools and techniques to the cybersecurity domain will also want to purchase this SpringerBrief.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2022
Idioma
Inglés
ISBN 10 
303115892X
ISBN 13 
9783031158926
Encuadernación
Tapa blanda
Número de edición
1
Número de páginas
58
Contacto del fabricante
no disponible
Persona responsable
CMN Printpool Inh. Mathis Neumann
https://www.also.com/ec/cms5/en_6000/6000/index.jsp

Friedrich-Karl-Str. 9
Hamburg
Alemania

Otras ediciones populares con el mismo título

9783031158940: Machine Learning for Cybersecurity: Innovative Deep Learning Solutions

Edición Destacada

ISBN 10:  3031158946 ISBN 13:  9783031158940
Editorial: Springer, 2022
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