Handbook of AI-Driven Threat Detection and Prevention
eBook - ePub

Handbook of AI-Driven Threat Detection and Prevention

A Holistic Approach to Security

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Handbook of AI-Driven Threat Detection and Prevention

A Holistic Approach to Security

About this book

In today's digital age, the risks to data and infrastructure have increased in both range and complexity. As a result, companies need to adopt cutting-edge artificial intelligence (AI) solutions to effectively detect and counter potential threats. This handbook fills the existing knowledge gap by bringing together a team of experts to discuss the latest advancements in security systems powered by AI. The handbook offers valuable insights on proactive strategies, threat mitigation techniques, and comprehensive tactics for safeguarding sensitive data.

Handbook of AI-Driven Threat Detection and Prevention: A Holistic Approach to Security explores AI-driven threat detection and prevention, and covers a wide array of topics such as machine learning algorithms, deep learning, natural language processing, and so on. The holistic view offers a deep understanding of the subject matter as it brings together insights and contributions from experts from around the world and various disciplines including computer science, cybersecurity, data science, and ethics. This comprehensive resource provides a well-rounded perspective on the topic and includes real-world applications of AI in threat detection and prevention emphasized through case studies and practical examples that showcase how AI technologies are currently being utilized to enhance security measures. Ethical considerations in AI-driven security are highlighted, addressing important questions related to privacy, bias, and the responsible use of AI in a security context. The investigation of emerging trends and future possibilities in AI-driven security offers insights into the potential impact of technologies like quantum computing and blockchain on threat detection and prevention.

This handbook serves as a valuable resource for security professionals, researchers, policymakers, and individuals interested in understanding the intersection of AI and security. It equips readers with the knowledge and expertise to navigate the complex world of AI-driven threat detection and prevention. This is accomplished by synthesizing current research, insights, and real-world experiences.

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Yes, you can access Handbook of AI-Driven Threat Detection and Prevention by Pankaj Bhambri,Jose Anand A.,A. Jose Anand in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Industrial Engineering. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover Page
  2. Half-Title Page
  3. Title Page
  4. Copyright Page
  5. Contents
  6. Preface
  7. Author Bios
  8. List of Contributors
  9. Chapter 1 Understanding AI and Machine Learning in Security
  10. Chapter 2 Data Collection and Preprocessing for Security
  11. Chapter 3 Feature Engineering for Threat Detection
  12. Chapter 4 Anomaly Detection with Artificial Intelligence
  13. Chapter 5 Signature-Based Security in Wireless Communication
  14. Chapter 6 Behavioral Analysis for Threat Detection
  15. Chapter 7 Network Security with Artificial Intelligence
  16. Chapter 8 Endpoint Security and Artificial Intelligence in the Financial Sector
  17. Chapter 9 Cloud Security and Artificial Intelligence
  18. Chapter 10 Adversarial Attacks on AI Security Systems: Investigating the Vulnerability of AI-Powered Security Solutions
  19. Chapter 11 Ethical Considerations and Privacy in AI-Powered Security
  20. Chapter 12 Artificial Intelligence in Financial Fraud Detection
  21. Chapter 13 Graph-Based Intelligent Cyber Threat Detection System
  22. Chapter 14 Future Trends in Artificial Intelligence Driven Security
  23. Chapter 15 Enhancing Cybersecurity with Distributed Models and Sparse Mixture of Experts
  24. Chapter 16 Anomaly Detection in SIEM Data: User Behavior Analysis with Artificial Intelligence
  25. Chapter 17 AI-Driven Security System for Biometric Surveillance
  26. Chapter 18 AI-Powered Predictive Analysis for Proactive Cyber Defense
  27. Chapter 19 Deep Learning Techniques for Intrusion Detection in Critical Infrastructure
  28. Chapter 20 Quantum Computing and AI Synergies: Strengthening Cybersecurity Resilience
  29. Chapter 21 Integrating AI with Blockchain for Decentralized Security and Threat Prevention
  30. Index