Cybersecurity is an area of increasing importance and a requirement for every software system. Problems in cybersecurity require adaptive data-driven solutions to be one step ahead of the attackers. AI offers methods of data analytics that enable us to efficiently recognize patterns in large-scale data. These methods have proven to be applicable in various cybersecurity problems, from authentication, detection of various types of cyberattacks in computer networks to the analysis of malicious executables.
This book begins by introducing the data analytics environment in Cybersecurity and gives context on where AI methods would fit in Cybersecurity projects. It takes you on a journey with an in-depth explanation of AI methods and tools that can be used to apply those methods, design and implement AI solutions. Furthermore, it contains descriptions of scenarios in Cybersecurity where AI methods are applicable, including exercises and code examples that give the reader hands-on skills in applications of AI to Cybersecurity problems. This makes you capable to not only recognize where AI methods can be applied, but also to design and implement efficient solutions using AI methods. To make you even more effective, we add some common pitfalls from real-world applications of AI in cybersecurity problems and give considerations on how to deal with them.

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Spis treści

Artificial Intelligence for Cybersecurity. Develop AI approaches to solve cybersecurity problems in your organization eBook -- spis treści

• 1. Big Data in Cybersecurity
• 2. Automation in Cybersecurity
• 3. Cybersecurity Data Analytics
• 4. AI, Machine Learning, and Statistics - A Taxonomy
• 5. AI Problems and Methods
• 6. Workflow, Tools, and Libraries in AI Projects
• 7. Malware and Network Intrusion Detection and Analysis
• 8. User and Entity Behavior Analysis
• 9. Fraud, Spam, and Phishing Detection
• 10. User Authentication and Access Control
• 11. Threat Intelligence
• 12. Anomaly Detection in Industrial Control Systems
• 13. Large Language Models and Cybersecurity
• 14. Data Quality and Its Usage in the AI and LLM Era
• 15. Correlation, Causation, Bias, and Variance
• 16. Evaluation, Monitoring, and Feedback Loop
• 17. Learning in a Changing and Adversarial Environment
• 18. Privacy, Accountability, Explainability, and Trust - Responsible AI
• 19. Summary