Kolosnjaji / Xiao / Xu Artificial Intelligence for Cybersecurity

Develop AI approaches to solve cybersecurity problems in your organization
1. Auflage 2024
ISBN: 978-1-80512-355-2
Verlag: Packt Publishing
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 364 Seiten

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.
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Autoren/Hrsg.

Kolosnjaji, Bojan

Xiao, Huang

Xu, Peng

Zarras, Apostolis

Leseproben

Preface

The cybersecurity threat landscape is evolving to include an increasing volume and variety of attacks. However, at the same time, the big data era and the proliferation of AI enable new methods and tools to tackle the challenge of detecting increasingly sophisticated malicious activity using large-scale data processing and AI for pattern recognition and enhanced data analytics. AI has already revolutionized multiple areas, some of the most well-known being healthcare, finance, and manufacturing. Currently, AI is making its way into cybersecurity as well through exciting and valuable applications.

In this book, we introduce the place of AI as a methodology in cybersecurity and how AI methods can help solve cybersecurity problems. We give a foundational knowledge of AI for those of you who are beginners in this area, starting with a theoretical underpinning. Following the theoretical chapters, we expand by looking at eight different application areas with six chapters dedicated to these applications. This way, we provide you with practical skills that can be used in concrete scenarios in real-world cybersecurity. After going through these chapters, you can immediately provide value to your organizations or advise your colleagues on how to make a difference with AI in cybersecurity.

Apart from describing the AI methods and going through various application scenarios, we include a part of the book with common pitfalls and challenges in applying AI for cybersecurity. This part helps you to be prepared for real-world applications of AI and the problems that are often overlooked by untrained practitioners, leading to inaccurate project planning and wasted time and effort. We help you recognize those problems and challenges and successfully overcome them to optimize the advantages you get with AI applications.

This book is authored by researchers with years of academic and practical experience applying AI in cybersecurity in various organizations. Publishing this book enables us to share our knowledge and experience in this area to train new experts and help improve cybersecurity overall. The combination of theoretical base and practical skills provided through this book is what has proven to be crucial in successful AI projects!

Who this book is for

This book is for cybersecurity or general IT professionals or students who are interested in AI technologies and how they can be applied in the cybersecurity context. It is useful both for readers with no knowledge about AI and experienced AI practitioners who can use it as a reference or to fill in the gaps in their skill set. It is good for both theoretically inclined and very practical readers who like hands-on exercises. This book can be used both for readers interested in solving concrete problems in their organization and for professionals who want to give advice, be a thought leader, and organize the introduction of AI as a capability in cybersecurity.

What this book covers

, , introduces the rising issue of handling large-scale data gathered by cybersecurity departments of various organizations and cybersecurity vendors. It describes the challenges of data processing and scale, as well as data quality, data governance, and similar.

, , emphasizes the importance of automation as a driver for efficiency in cybersecurity. We describe tools that help achieve automation, such as SIEM, SOAR, EDR, and IDS, that help experts define workflows and automate tasks. These tools are made with data analysis problems in mind and help automation at scale.

, , introduces the role of AI in advancing automation through intelligent data analytics on large-scale datasets. We describe challenges in this area that we will be solving throughout the book using AI methods and tools.

, , helps disambiguate the terms of AI, machine learning, and statistics, which can be difficult for beginners in this area. It also helps to get the foundations and an understanding of how AI applies to various datasets, and where the important limitations and challenges are.

, , builds on the basic terms of AI and helps you get more extensive knowledge and dive into concrete methods and how they work. It gives you the knowledge needed to recognize where different AI and ML methods are applicable and how to apply them.

, , describes the workflow of AI projects, from data collection and preprocessing to training and testing. Furthermore, it describes useful tools and libraries with examples in cybersecurity.

, , describes the problem of malware detection and network intrusion detection and how AI is applicable to solve it. We describe how AI makes a difference to improve detection performance and provide a hands-on exercise to improve your technical skills.

, , introduces the problem of finding a way to capture and analyze patterns in the behavior of users and hosts. We describe how AI methods can be used to model this behavior from raw event logs and detect anomalies that can point to cyberattacks.

, , contains a description of typical methods to detect transaction fraud, as well as spam and phishing emails using anomaly-based methods. These methods heavily benefit from AI, and we clarify how AI can be applied, and what the problems and challenges are in these use cases.

, , describes the problem and solutions on how to authenticate users and how to enable them to access only the resources that we intend them to use. We also describe AI methods that are applicable to these problems.

, , contains an overview of cyber-threat intelligence problems and techniques to extract information from various sources important to get an understanding of cyber threats. Furthermore, we describe how AI can help solve problems in this area, and we also provide a practical exercise to practice your knowledge of AI methods.

, , shows what kind of cybersecurity-relevant anomalies happen in industrial networks and how to detect them. AI methods are useful in this scenario as well, as they help us model regular behavior and detect anomalies.

, , introduces the recently popular topic of large language models (LLMs) as generative AI methods that found applications in cybersecurity. We describe the potential of applying LLMs in cybersecurity scenarios, the challenges in making these applications successful, and how to overcome them.

, , is an important chapter, as contemporary AI methods are data-driven and the success of the AI application heavily depends on data being fit for purpose. We describe methods of data quality management and challenges in this area.

, , covers these terms as they are important to know, and lack of understanding them often brings problems in AI applications. We give you an introduction and dive into the importance of differentiating correlation and causation, as well as describe the trade-off of bias and variance to help you avoid common pitfalls.

, , covers the very important parts of a machine learning workflow. We need to have proper methods for evaluation to describe performance and methods to monitor this performance. Furthermore, we often keep humans in the loop within the AI workflow to enhance our data or tune our models.

, , explains how many baseline AI methods contain assumptions about a static environment, and we need new techniques that enable the handling of changes in the data that happen naturally or because of adversarial activity. We present these techniques as they are especially important in cybersecurity applications.

, , explores responsible AI – recently, a very important topic as AI applications are adopted in various areas that influence people’s well-being and the development of society. We describe responsible AI and how to achieve it in general and in the cybersecurity...