Isbn: 9783032173669 - guide to ai for cybersecurity: principles, frameworks, and practical implementation (texts in computer science) (12 resultados)

Condición: New.

Buch. Condición: Neu. 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.…

Condición: New.

Hardcover. Condición: Brand New. 819 pages. 6.14x1.69x9.21 inches. In Stock.

Hardcover. Condición: new. Hardcover. 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 incidentsincluding 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 adoptionPowerPoint 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.Muthu Ramachandran is Research Consultant at Forti5 Technologies Ltd, UK, and Visiting Professor Extraordinarius at University of South Africa. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 592 pp. Englisch.…

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Hardcover. Condición: new. Hardcover. 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 incidentsincluding 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 adoptionPowerPoint 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.Muthu Ramachandran is Research Consultant at Forti5 Technologies Ltd, UK, and Visiting Professor Extraordinarius at University of South Africa. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Condición: New. Print on Demand.

Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 incidentsincluding 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: 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.Muthu Ramachandran is Research Consultant at Forti5 Technologies Ltd, UK, and Visiting Professor Extraordinarius at University of South Africa.Springer Nature Customer Service Center GmbH, Europaplatz 3,69115 Heidelberg, Germany, Heidelberg 592 pp. Englisch.…

Condición: New. PRINT ON DEMAND.

Hardcover. Condición: new. Hardcover. 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 incidentsincluding 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 adoptionPowerPoint 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.Muthu Ramachandran is Research Consultant at Forti5 Technologies Ltd, UK, and Visiting Professor Extraordinarius at University of South Africa. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…