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Machine Learning Techniques for Cybersecurity

Name: Machine Learning Techniques for Cybersecurity
Brand: Springer, Berlin
SKU: 9783031282584
Price: 186.85 PLN
Availability: InStock

Autor: Elisa Bertino, Sonam Bhardwaj, Fabrizio Cicala, Sishuai Gong, Imtiaz Karim, Charalampos Katsis, Hyunwoo Lee, Adrian Shuai Li, Ashraf Y. Mahgoub

Język:

Oprawa: Twarda

Wydawca: Springer, Berlin

Dostępność: Dostępna u dostawcy

Wysyłamy za 10-13 dni

186.85 zł

This book explores machine learning (ML) defenses against the many cyberattacks that make our workpl...

Informacje o książce

Autor

Elisa Bertino, Sonam Bhardwaj, Fabrizio Cicala, Sishuai Gong, Imtiaz Karim, Charalampos Katsis, Hyunwoo Lee, Adrian Shuai Li, Ashraf Y. Mahgoub

Język

Angielski

Oprawa

Książka - Twarda

Data wydania

2023

strony

180

EAN

9783031282584

Enbook ID

42903670

Wydawca

Springer, Berlin

Waga

489

Wymiary

168 x 240

Pełny opis

This book explores machine learning (ML) defenses against the many cyberattacks that make our workplaces, schools, private residences, and critical infrastructures vulnerable as a consequence of the dramatic increase in botnets, data ransom, system and network denials of service, sabotage, and data theft attacks. The use of ML techniques for security tasks has been steadily increasing in research and also in practice over the last 10 years. Covering efforts to devise more effective defenses, the book explores security solutions that leverage machine learning (ML) techniques that have recently grown in feasibility thanks to significant advances in ML combined with big data collection and analysis capabilities. Since the use of ML entails understanding which techniques can be best used for specific tasks to ensure comprehensive security, the book provides an overview of the current state of the art of ML techniques for security and a detailed taxonomy of security tasks and corresponding ML techniques that can be used for each task. It also covers challenges for the use of ML for security tasks and outlines research directions. While many recent papers have proposed approaches for specific tasks, such as software security analysis and anomaly detection, these approaches differ in many aspects, such as with respect to the types of features in the model and the dataset used for training the models. In a way that no other available work does, this book provides readers with a comprehensive view of the complex area of ML for security, explains its challenges, and highlights areas for future research. This book is relevant to graduate students in computer science and engineering as well as information systems studies, and will also be useful to researchers and practitioners who work in the area of ML techniques for security tasks.