Artificial Intelligence and Machine Learning in Cybersecurity by Young PhD, Richard Gwashy;

Short description:

This book offers readers an in-depth understanding of how AI and ML are reshaping the cybersecurity landscape. It begins with foundational concepts, explaining AI and ML's principles and their transformative potential within various sectors, particularly cybersecurity.

Long description:

Artificial Intelligence and Machine Learning in Cybersecurity: A Comprehensive Guide to Improving Cybersecurity Protocol is a comprehensive exploration of the intersection between cutting-edge technology and cybersecurity practices. This book offers readers an in-depth understanding of how AI and ML are reshaping the cybersecurity landscape. It begins with foundational concepts, explaining AI and ML’s principles and their transformative potential within various sectors, particularly cybersecurity. This book uniquely combines theoretical insights and practical applications, making it an essential resource for graduate students and cybersecurity professionals eager to expand their knowledge and skills.

The book’s uniqueness lies in its detailed analysis of how AI and machine learning can predict and counteract emerging threats in real time, shifting the paradigm from reactive to proactive cybersecurity measures. By delving into a wide range of topics, such as AI-Powered Intrusion Detection and Prevention Systems (IDPS) and Endpoint Security, the author provides case studies and examples from sectors like finance and healthcare. This hands-on approach not only illustrates successful implementations but also highlights potential challenges, offering balanced perspectives and strategies to overcome hurdles. The inclusion of ethical considerations around AI usage in cybersecurity further distinguishes it as a forward-thinking guide.

As cyber threats continue to evolve, the need for advanced AI and ML methodologies becomes increasingly critical. This book addresses this urgency by equipping readers with contemporary knowledge and tools necessary to leverage these technologies effectively. The discussion of future trends, such as AI-powered quantum security and necessary policy implications, ensures that readers are well-prepared to navigate the complexities of cybersecurity in the coming decades. Ultimately, it serves as both an educational textbook for students and a practical guide for cyber practitioners, offering a roadmap for implementing AI-driven cybersecurity solutions that enhance threat detection, response, and prevention.

Table of Contents:

Chapter 1: Introduction to AI, ML, and Cybersecurity Chapter 2: The Cyber Threat Landscape in the 21st Century Chapter 3: Foundations of Artificial Intelligence and Machine Learning in Cybersecurity Chapter 4: Predictive Analytics and Threat Intelligence with AI and ML Chapter 5: Automating Security Protocols Using AI and ML Chapter 6: AI-Powered Intrusion Detection and Prevention Systems (IDPS) Chapter 7: AI and ML in Endpoint Security and Zero Trust Models Chapter 8: Enhancing Network Security with AI and ML Chapter 9: AI and ML in Combatting Cybercrime and Fraud Chapter 10: AI and ML in Cybersecurity Operations and Security Operations Centers (SOCs) Chapter 11: Ethical Considerations and Risks of AI/ML in Cybersecurity Chapter 12: Conclusion: Leveraging Artificial Intelligence and Machine Learning to Improve Cybersecudity Protocols