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Guide to AI for cybersecurity : principles, frameworks, and practical implementation / Muthu Ramachandran
Springer Nature - Springer Computer Science eBooks 2026 English International Available online
View online- Format:
- Book
- Author/Creator:
- Ramachandran, Muthu, Author.
- Series:
- Texts in computer science 1868-095X
- Texts in computer science, 1868-095X
- Language:
- English
- Subjects (All):
- Computer security.
- Artificial intelligence.
- artificial intelligence.
- Physical Description:
- 1 online resource : illustrations
- Place of Publication:
- Cham, Switzerland : Springer, [2026]
- Summary:
- "With cybercrime costs exceeding 10.5 trillion dollars 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"-- Springer Nature Link
- Contents:
- Modern threat landscape and AI opportunities
- Cybersecurity frameworks in the AI era
- AI security architecture and infrastructure
- Machine learning for threat
- Advanced analytics and threat intelligence
- Implementation case studies
- AI-enhanced secure software development lifecycle and secure requirements engineering
- Threat modeling and secure design best practices for AI-enhanced applications : New vectors and mitigations
- AI-powered secure software development and best practice static code analysis : Automated vulnerability detection
- Security testing, validation, and deployment automation
- Secure software development frameworks and standards : A critical analysis of national and international standards
- Data protection and privacy in AI systems
- Application security with machine learning
- Secure coding best practices for AI application development : A comprehensive framework approach
- Automated incident response and orchestration
- Ethics, governance, risks, compliance, and sovereignty of AI for cybersecurity
- AI security and adversarial defenses
- Future directions and emerging threats in AI-enabled cybersecurity
- Notes:
- Includes bibliographical references
- Online resource; title from PDF title page (Springer Nature Link, viewed July 30, 2026)
- Other Format:
- Print version: Ramachandran, Muthu Guide to AI for cybersecurity
- ISBN:
- 9783032173676
- 3032173671
- OCLC:
- 1599129317
- Access Restriction:
- Restricted for use by site license
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