£116.98

Springer - Machine Learning in Cyber Trust Book

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Description

Networked computer systems face constant threats from cyber attacks that can disrupt functions, corrupt data, or expose private information. As these vulnerabilities increase, the need for advanced defense mechanisms becomes more urgent. This book by Springer addresses these challenges by treating cybersecurity tasks as learning problems that can be solved using machine learning algorithms. This publication features original research from leading experts in the field. It explores how different machine learning methods can be applied to improve the reliability, security, performance, and privacy of cyberspace. Readers can explore various applications and learn how to approach complex security issues through the lens of modern computational learning. This resource is designed to help professionals and students understand the intersection of artificial intelligence and cybersecurity to build more resilient digital environments.

Key Features

Covers essential applications of machine learning methods to improve the reliability and security of modern cyberspace.

Provides original research materials contributed by leading experts in the field of machine learning and cybersecurity.

Addresses critical issues regarding data privacy and system performance in networked computer environments.

Explores how cyber-based system tasks can be formulated as learning problems to combat data corruption and attacks.

Helps readers identify specific types of machine learning approaches suitable for different security challenges.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
05 November 2010
Listed Since
16 December 2010

Barcode

No barcode data available

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