Machine Learning for Malware Detection (Paperback)

Taylor Royce

ISBN 13: 9798281853163

Verlag: Independently Published, 2025

Beschreibung

Paperback. Machine Learning for Malware Detection: Strategies, Models, and Applications Reactive defenses are no longer adequate as the cybersecurity environment gets more complicated and adversaries become more skilled. A state-of-the-art, professionally grounded investigation of how artificial intelligence, in particular machine learning, can be used to proactively identify, categorize, and react to malware threats in real-time is provided by Machine Learning for Malware Detection: Strategies, Models, and Applications. Data scientists, threat analysts, cybersecurity professionals, and technology executives who understand the critical need for intelligent, scalable defenses in today's digital infrastructure are the target audience for this book. It provides a thorough and useful road map for incorporating machine learning into contemporary malware detection processes while being mindful of the operational, moral, and legal issues that come with AI-powered systems. This book explores the entire lifecycle of intelligent malware detection, from data gathering and feature engineering to model evaluation, adversarial resilience, and ethical deployment, rather than concentrating only on algorithms or superficial trends. Every chapter is thoughtfully organized to provide practical insights derived from current research, real-world problems, and tried-and-true tactics. The following topics will be thoroughly understood by readers: The advantages and disadvantages of machine learning models in dynamic threat situationsMethods for adversarial hardening and identifying malware that evades artificial intelligence; strategies for reducing false positives and preserving model reliability over timeStrategic considerations for creating resilient, future-ready cyber defense ecosystems; the use of machine learning into larger threat intelligence and incident response frameworksThis book stands out for its dedication to professionalism, depth, and clarity. In addition to being technically solid, the content is contextualized within the larger goals of safeguarding user privacy, defending digital assets, and facilitating the appropriate use of AI in security operations. In a time when machine learning may be used as a weapon and a shield, Machine Learning for Malware Detection: Strategies, Models, and Applications is more than just a technical handbook; it is a strategic manual for creating intelligent, robust, and moral cybersecurity systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Bestandsnummer des Verkäufers 9798281853163

Bibliografische Details

• Titel: Machine Learning for Malware Detection (Paperback)
• Verlag: Independently Published
• Erscheinungsjahr: 2025
• Sprache: Englisch
• ISBN-13: 9798281853163
• Einband: Paperback
• Zustand: new

Über diesen Titel

Inhaltsangabe

Machine Learning for Malware Detection: Strategies, Models, and Applications

Reactive defenses are no longer adequate as the cybersecurity environment gets more complicated and adversaries become more skilled. A state-of-the-art, professionally grounded investigation of how artificial intelligence, in particular machine learning, can be used to proactively identify, categorize, and react to malware threats in real-time is provided by Machine Learning for Malware Detection: Strategies, Models, and Applications.

Data scientists, threat analysts, cybersecurity professionals, and technology executives who understand the critical need for intelligent, scalable defenses in today's digital infrastructure are the target audience for this book. It provides a thorough and useful road map for incorporating machine learning into contemporary malware detection processes while being mindful of the operational, moral, and legal issues that come with AI-powered systems.

This book explores the entire lifecycle of intelligent malware detection, from data gathering and feature engineering to model evaluation, adversarial resilience, and ethical deployment, rather than concentrating only on algorithms or superficial trends. Every chapter is thoughtfully organized to provide practical insights derived from current research, real-world problems, and tried-and-true tactics.

The following topics will be thoroughly understood by readers:

• The advantages and disadvantages of machine learning models in dynamic threat situations
• Methods for adversarial hardening and identifying malware that evades artificial intelligence; strategies for reducing false positives and preserving model reliability over time
• Strategic considerations for creating resilient, future-ready cyber defense ecosystems; the use of machine learning into larger threat intelligence and incident response frameworks

This book stands out for its dedication to professionalism, depth, and clarity. In addition to being technically solid, the content is contextualized within the larger goals of safeguarding user privacy, defending digital assets, and facilitating the appropriate use of AI in security operations.

In a time when machine learning may be used as a weapon and a shield, Machine Learning for Malware Detection: Strategies, Models, and Applications is more than just a technical handbook; it is a strategic manual for creating intelligent, robust, and moral cybersecurity systems.