Artificial Intelligence for Cybersecurity
Artificial Intelligence for Cybersecurity
Develop AI approaches to solve cybersecurity problems in your organization
About the 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.
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
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