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Book Overview & Buying
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Table Of Contents
Artificial Intelligence for Cybersecurity
By :
Artificial Intelligence for Cybersecurity
By:
Overview of this book
Artificial intelligence offers data analytics methods that enable us to efficiently recognize patterns in large-scale data. These methods can be applied to various cybersecurity problems, from authentication and the detection of various types of cyberattacks in computer networks to the analysis of malicious executables.
Written by a machine learning expert, this book introduces you to the data analytics environment in cybersecurity and shows you where AI methods will fit in your cybersecurity projects. The chapters share an in-depth explanation of the AI methods along with tools that can be used to apply these methods, as well as design and implement AI solutions. You’ll also examine various cybersecurity scenarios where AI methods are applicable, including exercises and code examples that’ll help you effectively apply AI to work on cybersecurity challenges. The book also discusses common pitfalls from real-world applications of AI in cybersecurity issues and teaches you how to tackle them.
By the end of this book, you’ll be able to not only recognize where AI methods can be applied, but also design and execute efficient solutions using AI methods.
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Table of Contents (27 chapters)
Preface
Chapter 1: Big Data in Cybersecurity
Chapter 2: Automation in Cybersecurity
Chapter 3: Cybersecurity Data Analytics
Part 2: AI and Where It Fits In
Chapter 4: AI, Machine Learning, and Statistics - A Taxonomy
Chapter 5: AI Problems and Methods
Chapter 6: Workflow, Tools, and Libraries in AI Projects
Part 3: Applications of AI in Cybersecurity
Chapter 7: Malware and Network Intrusion Detection and Analysis
Chapter 8: User and Entity Behavior Analysis
Chapter 9: Fraud, Spam, and Phishing Detection
Chapter 10: User Authentication and Access Control
Chapter 11: Threat Intelligence
Chapter 12: Anomaly Detection in Industrial Control Systems
Chapter 13: Large Language Models and Cybersecurity
Part 4: Common Problems When Applying AI in Cybersecurity
Chapter 14: Data Quality and its Usage in the AI and LLM Era
Chapter 15: Correlation, Causation, Bias, and Variance
Chapter 16: Evaluation, Monitoring, and Feedback Loop
Chapter 17: Learning in a Changing and Adversarial Environment
Chapter 18: Privacy, Accountability, Explainability, and Trust – Responsible AI
Part 5: Final Remarks and Takeaways
Chapter 19: Summary
Index