Short description:

In today's digital age, companies need to adopt cutting-edge artificial intelligence solutions to effectively detect and counter potential threats. This handbook brings together a team of experts to discuss insights on proactive strategies, threat mitigation techniques, and comprehensive tactics for safeguarding sensitive data.

Long description:

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.

Table of Contents:

1. Understanding AI and Machine Learning in Security.  2. Data Collection and Preprocessing for Security.  3. Feature Engineering for Threat Detection.  4. Anomaly Detection with Artificial Intelligence.  5. Signature-based Security in Wireless Communication.  6. Behavioral Analysis for Threat Detection.  7. Network Security with Artificial Intelligence.  8. Endpoint Security and Artificial Intelligence in the Financial Sector.  9.  Cloud Security and Artificial Intelligence.  10. Adversarial Attacks on AI Security Systems.  11. Ethical Considerations and Privacy in Artificial Intelligence Powered Security Systems.  12. Artificial Intelligence in Financial Fraud Detection.  13. Graph-based Intelligent Cyber Threat Detection System.  14. Future Trends in Artificial Intelligence Driven Security.  15. Enhancing Cybersecurity with Distributed Models and Sparse Mixture of Experts.  16. Anomaly Detection in SIEM Data: User Behavior Analysis with Artificial Intelligence.  17. AI-Driven Security System for Biometric Surveillance.  18. AI-Powered Predictive Analysis for Proactive Cyber Defense.  19. Deep Learning Techniques for Intrusion Detection in Critical Infrastructure.  20. Quantum Computing and AI Synergies: Strengthening Cybersecurity Resilience.  21. Integrating AI with Blockchain for Decentralized Security and Threat Prevention.