Table of Contents
- Big Data in Cybersecurity
- Automation in Cybersecurity
- Cybersecurity Data Analytics
- AI, Machine Learning, and Statistics - A Taxonomy
- AI Problems and Methods
- Workflow, Tools, and Libraries in AI Projects
- Malware and Network Intrusion Detection and Analysis
- User and Entity Behavior Analysis
- Fraud, Spam, and Phishing Detection
- User Authentication and Access Control
- Threat Intelligence
- Anomaly Detection in Industrial Control Systems
- Large Language Models and Cybersecurity
- Data Quality and Its Usage in the AI and LLM Era
- Correlation, Causation, Bias, and Variance
- Evaluation, Monitoring, and Feedback Loop
- Learning in a Changing and Adversarial Environment
- Privacy, Accountability, Explainability, and Trust - Responsible AI
- Summary
