This is the code repository for Artificial Intelligence for Cybersecurity, published by Packt.
Develop AI approaches to solve cybersecurity problems in your organization
This book teaches you how to recognize problems in cybersecurity where AI adds value, design and implement efficient solutions, and understand where and when to apply these methods to solve cybersecurity problems.
This book covers the following exciting features:
- 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
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
import tensorflow as tf
import tensorflow_datasets as tfds
(pcap_data_train, pcap_data_test), pcap_ds_info = tfds.load(
‘pcap_mnist’, split=[‘train’, ‘test’],
shuffle_files=True, as_supervised=True, with_info=True,
)Following is what you need for this book: 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.
With the following software and hardware list you can run all code files present in the book (Chapter 1-19).
| Chapter | Software required | OS required |
|---|---|---|
| 1-19 | Python 3 | Windows, macOS, or Linux |
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.
Huang Xiao 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.
Apostolis Zarras is a cybersecurity researcher with a rich academic background. He has served as a faculty member at both Delft University of Technology and Maastricht University. Dr. Zarras earned his PhD in IT security from Ruhr-University Bochum, where he honed his expertise in systems, networks, and web security. His research is driven by a passion for developing innovative security paradigms, architectures, and software that fortify ICT and IoT systems. Beyond his technical contributions, Dr. Zarras delves into the dark web and its underground markets, uncovering and combating malicious activities to bolster global cybersecurity. His work is dedicated to advancing IT security and protecting users and systems from emerging cyber threats.