Druck auf Anfrage Neuware - Printed after ordering - With cybercrime costs exceeding $10.5 trillion annually and ransomware attacks predicted every two seconds by 2031, traditional signature-based security has reached critical breaking points. Guide to AI for Cybersecurity provides the essential roadmap for harnessing artificial intelligence as a force multiplier against sophisticated, AI-powered threats.This comprehensive textbook bridges the gap between artificial intelligence theory and practical cybersecurity applications through 18 chapters organized around an innovative detection, response, prediction, and prevention (DRPP) framework. Drawing from recent high-impact incidents including the 2025 Collins Aerospace cyberattack, the Marks & Spencer ransomware attack, and the Co-op data breach readers progress from foundational concepts to advanced implementations, gaining hands-on experience with production-ready code examples, real-world case studies, and comprehensive deployment guidance for AI-powered security solutions.Topics and features: Introduces the DRPP framework for systematically implementing AI security across the complete security lifecycle Includes complete instructor resources for flexible course adoption PowerPoint slides, laboratory exercises, assessment questions, and implementation projects Provides comprehensive coverage of machine learning (ML) for threat detection, adversarial AI defenses, and automated incident response Integrates ethics, governance, and regulatory compliance (GDPR, CCPA, AI Act) throughout, with dedicated coverage of privacy-preserving techniques Offers detailed guidance on integrating AI capabilities with industry standards while maintaining compliance requirementsThis essential textbook/guide provides comprehensive coverage suitable for graduate students in computer science, cybersecurity, or AI/ML programs, as well as cybersecurity professionals seeking to master AI-powered defense techniques. Software architects building secure AI systems, academic instructors developing AI security courses, and researchers investigating adversarial machine learning also will find the volume invaluable.Muth.

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Bibliographic Details

Title: Guide to AI for Cybersecurity : Principles, Frameworks, and Practical Implementation
Publisher: Springer Nature Switzerland AG Okt 2026
Publication Date: 2026
Language: English
ISBN 10: 3032173663
ISBN 13: 9783032173669
Binding: Buch
Condition: Neu
Dimensions: 235 millimeters width by 155 millimeters height by 35 millimeters depth
Weight: 1322 grams

About this title

Synopsis

With cybercrime costs exceeding $10.5 trillion annually and ransomware attacks predicted every two seconds by 2031, traditional signature-based security has reached critical breaking points. Guide to AI for Cybersecurity provides the essential roadmap for harnessing artificial intelligence as a force multiplier against sophisticated, AI-powered threats.

This comprehensive textbook bridges the gap between artificial intelligence theory and practical cybersecurity applications through 18 chapters organized around an innovative detection, response, prediction, and prevention (DRPP) framework. Drawing from recent high-impact incidents—including the 2025 Collins Aerospace cyberattack, the Marks & Spencer ransomware attack, and the Co-op data breach —readers progress from foundational concepts to advanced implementations, gaining hands-on experience with production-ready code examples, real-world case studies, and comprehensive deployment guidance for AI-powered security solutions.

Topics and features:

• Introduces the DRPP framework for systematically implementing AI security across the complete security lifecycle

• Includes complete instructor resources for flexible course adoption—PowerPoint slides, laboratory exercises, assessment questions, and implementation projects

• Provides comprehensive coverage of machine learning (ML) for threat detection, adversarial AI defenses, and automated incident response

• Integrates ethics, governance, and regulatory compliance (GDPR, CCPA, AI Act) throughout, with dedicated coverage of privacy-preserving techniques

• Offers detailed guidance on integrating AI capabilities with industry standards while maintaining compliance requirements

This essential textbook/guide provides comprehensive coverage suitable for graduate students in computer science, cybersecurity, or AI/ML programs, as well as cybersecurity professionals seeking to master AI-powered defense techniques. Software architects building secure AI systems, academic instructors developing AI security courses, and researchers investigating adversarial machine learning also will find the volume invaluable.

Muthu Ramachandran is Research Consultant at Forti5 Technologies Ltd, UK, and Visiting Professor Extraordinarius at University of South Africa.

About the Author

Professor Muthu Ramachandran is currently a Cybersecurity, AI, and Blockchain Research
Consultant at Forti5 Technologies, UK, and a Professor Extraordinarius at University of South Africa
(UNISA). Muthu is a certified IASME CE, ICA, IoT Cyber & IoT Assurance professional with
extensive experience spanning academia, industry research, and standards development.
Previously, Muthu served as Associate Professor at Leeds Beckett University for 18 years and held
visiting professorships at Indian Institute of Technology (ISM) Dhanbad and University of
Southampton. His industrial experience includes nearly eight years at Philips Research Labs, Volantis
Systems Ltd, and DRDO Hyderabad.
As a Fellow of BCS, Senior Fellow of Advance HE, and Senior Member of IEEE and ACM, Muthu has
authored numerous books, book chapters, journal articles, and conference papers. He has chaired and
delivered keynote speeches at major conferences including SE-CLOUD, SECE, IoTBDS, and DSC.

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