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Full Description
Cybernetic Shield: Securing the Future of Machine Intelligence explores the critical intersection between cybersecurity and the rapid advancements in artificial intelligence. This book addresses the growing need for robust security measures in AI-driven systems by presenting cutting-edge strategies and frameworks that ensure the integrity, trust, and resilience of machine intelligence. It delves into a wide array of emerging cybersecurity challenges—from securing AI-integrated learning platforms in digital education to safeguarding blockchain-based solutions and tackling vulnerabilities in IoT networks. The book offers practical insights into protecting against cyber threats that threaten the changing digital landscape, guaranteeing a future where intelligent technologies can be safely harnessed for innovation and advancement. It does this by drawing on both theoretical analysis and real-world case studies.
This book offers an in-depth exploration of the latest cybersecurity techniques tailored for AI applications. The chapters cover a range of topics, from securing machine learning platforms and mitigating privacy risks in AI-driven healthcare to implementing decentralized blockchain systems and safeguarding critical systems from AI-driven vulnerabilities. Readers will benefit from practical solutions to pressing cybersecurity challenges, expert insights on governance frameworks, and real-world examples of how emerging technologies like IoT, AI, and blockchain are reshaping the future of cybersecurity. The book serves as an essential resource for anyone looking to stay ahead of the curve in the rapidly evolving digital landscape.
Cybernetic Shield: Securing the Future of Machine Intelligence is specifically designed for professionals, researchers, and students in the fields of cybersecurity, AI, and digital technologies. It caters to individuals involved in securing AI systems, blockchain development, and digital infrastructures across various industries, including healthcare, finance, and education. The book is also highly relevant to policymakers, legal experts, and technology leaders who are seeking guidance on creating frameworks to protect the future of machine intelligence. With its comprehensive approach, it appeals to anyone interested in understanding the critical role of cybersecurity in the age of AI and machine learning.
Contents
1 Cyber Defense Strategies for Ensuring Electoral Trust and Integrity. 2. Cyber Barriers: Leveraging IoT-Driven Security Systems for Enhanced Wildlife Protection. 3. Ciphering Authenticity: Unveiling Signature Forgery with CNN. 4. AI Governance and Cybersecurity: A Legal Framework for Safeguarding the Future of Machine Intelligence. 5. Securing AI-Integrated Learning Platform: Cyber Threats in the Age of Digital Education. 6. Blockchain-Decentralized approach for Crowdfunding using ECDSA with P-256 and SHA-256 algorithm. 7. Cyberbullying Text Classification Using NLP - Algorithm-Based Techniques. 8. Enhancing Election Integrity with Cybersecurity Solutions. 9. Mitigating Privacy and Security Risks of Federated Learning in Smart Healthcare: Analysis. 10. Privacy-Preserving Federated Transfer Learning for Brain Tumor Multi-Classification in AI-Driven Healthcare. 11. Futuristic Health Care Management System Using AI. 12. Open-Access Datasets for Brain-Computer Interfaces Research. 13. Navigating the Risks: Cybersecurity Challenges Stemming from AI Breaches in Critical Systems. 14. Machine Learning Based Model for Detecting Cyber Attack Vulnerabilities. 15. Exploring Big Tech Vulnerabilities: Transparent Blockchain Harvesting. 16. Comprehensive Guide to Blockchain Technology: Ecosystems, Implementation, Governance, and Innovations. 17. Cloud Based Business Application with Automated Resource Scaling and Optimized Job Scheduling. 18. Addressing Data Security and Privacy Challenges: A Critical Analysis of Convolutional Neural Networks (CNN) Applications. 19. Cybernetic Shield and IoT-Based Vibration Fencing for Wildlife Protection. 20. Enhanced Object Detection in Underwater Imaging using Deep Learning Method. 21. Explaining and Adapting: Self-Healed AI Security Systems for Future Cybersecurity.
