Librería Portfolio Librería Portfolio

Búsqueda avanzada

TIENE EN SU CESTA DE LA COMPRA

0 productos

en total 0,00 €

ADVERSARY-AWARE LEARNING TECHNIQUES AND TRENDS IN CYBERSECURITY
Título:
ADVERSARY-AWARE LEARNING TECHNIQUES AND TRENDS IN CYBERSECURITY
Subtítulo:
Autor:
DASGUPTA, P
Editorial:
SPRINGER VERLAG
Año de edición:
2021
ISBN:
978-3-030-55691-4
Páginas:
2021
134,95 €

 

Sinopsis

Cutting-edge technology and tools for developing secure, safe and reliable, machine learning-enabled software systems
Techniques to address different aspects of adversarial machine learning through covering diverse topics including game-playing AI, deception in AI, generative adversarial network (GAN), big data, network security, and human machine teaming
Written by eminent researchers from premier US universities and US federal research laboratories



This book is intended to give researchers and practitioners in the cross-cutting fields of artificial intelligence, machine learning (AI/ML) and cyber security up-to-date and in-depth knowledge of recent techniques for improving the vulnerabilities of AI/ML systems against attacks from malicious adversaries. The ten chapters in this book, written by eminent researchers in AI/ML and cyber-security, span diverse, yet inter-related topics including game playing AI and game theory as defenses against attacks on AI/ML systems, methods for effectively addressing vulnerabilities of AI/ML operating in large, distributed environments like Internet of Things (IoT) with diverse data modalities, and, techniques to enable AI/ML systems to intelligently interact with humans that could be malicious adversaries and/or benign teammates. Readers of this book will be equipped with definitive information on recent developments suitable for countering adversarial threats in AI/ML systems towards making them operate in a safe, reliable and seamless manner.