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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
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.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
Da: California Books, Miami, FL, U.S.A.
Condizione: New. Codice articolo I-9783032173669
Quantità: Più di 20 disponibili
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Buch. Condizione: 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. Codice articolo 9783032173669
Quantità: 2 disponibili
Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. Codice articolo 26405412743
Quantità: 4 disponibili
Da: Majestic Books, Hounslow, Regno Unito
Condizione: New. Codice articolo 408790104
Quantità: 4 disponibili
Da: Biblios, Frankfurt am main, HESSE, Germania
Condizione: New. Codice articolo 18405412749
Quantità: 4 disponibili
Da: moluna, Greven, Germania
Condizione: New. Codice articolo 2789523052
Quantità: Più di 20 disponibili
Da: Revaluation Books, Exeter, Regno Unito
Hardcover. Condizione: Brand New. 819 pages. 6.14x1.69x9.21 inches. In Stock. Codice articolo x-3032173663
Quantità: 2 disponibili
Da: AHA-BUCH GmbH, Einbeck, Germania
Buch. Condizione: 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. Codice articolo 9783032173669
Quantità: 2 disponibili