Gain well-rounded knowledge of AI methods in cybersecurity and obtain hands-on experience in implementing them to bring value to your organization
Key Features
- Familiarize yourself with AI methods and approaches and see how they fit into cybersecurity
- Learn how to design solutions in cybersecurity that include AI as a key feature
- Acquire practical AI skills using step-by-step exercises and code examples
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description
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.
What you will learn
- Recognize AI as a powerful tool for intelligence analysis of cybersecurity data
- Explore all the components and workflow of an AI solution
- Find out how to design an AI-based solution for cybersecurity
- Discover how to test various AI-based cybersecurity solutions
- Evaluate your AI solution and describe its advantages to your organization
- Avoid common pitfalls and difficulties when implementing AI solutions
Who this book is for
This book is for machine learning practitioners looking to apply their skills to overcome cybersecurity challenges. Cybersecurity workers who want to leverage machine learning methods will also find this book helpful. Fundamental concepts of machine learning and beginner-level knowledge of Python programming are needed to understand the concepts present in this book. Whether you’re a student or an experienced professional, this book offers a unique and valuable learning experience that will enable you to protect your network and data against the ever-evolving threat landscape.
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
Bojan Kolosnjaji is a researcher working at the intersection of artificial intelligence (AI) and cybersecurity. He has obtained his master's and PhD degrees in computer science from the Technical University of Munich (TUM), where he conducted research in anomaly detection methods in constrained environments. Bojan's academic work deals with anomaly detection problems in multiple cybersecurity-relevant scenarios, and the design of AI-based solutions to these problems. Bojan is currently working as a principal engineer in cybersecurity sciences and analytics, helping various cybersecurity teams deal with large-scale data, adopt AI practices and solutions, and understand security challenges in AI systems.
Xiao Huang holds a doctorate in computer science from TUM. He is also a visiting scholar at Stanford University. His main research interests include adversarial machine learning (ML), reinforcement learning, anomaly detection, trusted AI, and AI applications in cybersecurity. Huang has published several top-tier conference and journal papers with over a thousand citations in both the ML and security domains. He led the ML research group at Fraunhofer AISEC Institute in Munich and also worked as a research scientist at Bosch Center for AI. He managed a data scientist team that designed and developed ML systems to tackle different cybersecurity problems.
Peng Xu has focused on AI for system security, large language model (LLM) security, graph neural networks, program analysis, compiler design, optimization, and cybersecurity. He completed his master's at the Chinese Academy of Science in 2013 and pursued a PhD in IT security at TUM from 2015 to 2019. He is currently awaiting his dissertation defense. Peng's research topics include malware detection, private computation, and software vulnerability mitigation using compiler-based approaches. Peng is currently working as a principal engineer in compiler optimization and programming LLMs, especially on the topics of using LLMs to generate code blocks to detect malicious code as well as bug localization.
