Artificial Intelligence for Cybersecurity: Develop AI approaches to solve cybersecurity problems in your organization

Artificial Intelligence for Cybersecurity

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Contributors

About the authors

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.

About the reviewers

Hemanath Kumar J is a seasoned data enthusiast with extensive experience in developing and implementing ML models, GenAI models, data visualization, and analytics solutions. With a diverse background in transportation, education, finance, and healthcare, he has consistently delivered solutions with data-driven strategies, which enhanced decision-making processes with high accuracy and operational efficiency. As a technical reviewer for Packt Publications, he brings his comprehensive expertise to this book, ensuring accuracy and clarity. He would like to acknowledge his family, mentors, and friends for their unwavering support and encouragement throughout this project.

Pranav Khare is a business and technology professional with over 14 years of experience in product management, business strategy, and software engineering. Starting his journey at Infosys as a software engineer, Pranav’s curiosity shifted from the How? to the Why?, guiding him on a path that led from technical execution to the strategic vision of a product manager. Now, as a senior product manager at Docusign, he drives innovation in digital identity verification, employing AI/ML-based solutions to meet diverse customer, security, and compliance needs. He holds an MBA from Georgetown University and a Bachelor of Engineering in electronics and communication.

Table of Contents

Preface

Part 1: Data-Driven Cybersecurity and AI

1

Big Data in Cybersecurity

Technical requirements

What is big data?

Big data challenges in cybersecurity

The velocity of data in cyberspace

Diverse data types in cyberspace

The veracity of data in cyberspace

Advanced analytical techniques and tools

Resource constraints

Big data applications in cybersecurity

Big data technologies for cybersecurity

Summary

2

Automation in Cybersecurity

Tools and technologies against threats

Security information and event management (SIEM)

Intrusion detection and prevention systems (IDPSs)

Endpoint detection and response (EDR)

Security orchestration, automation, and response (SOAR)

The importance of automation in cybersecurity

Examples of automated cybersecurity tools

Potential drawbacks and challenges of automation

The future of automation in cybersecurity

Ethical considerations

Summary

3

Cybersecurity Data Analytics

AI in data analytics

Types of AI used in cybersecurity data analytics

Applications of AI

Challenges of using AI

The role of analysts

The regulatory landscape

Summary

Part 2: AI and Where It Fits In

4

AI, Machine Learning, and Statistics - A Taxonomy

Technical requirements

A brief introduction to AI history

The relation to statistical learning theory

ML – classifying taxonomy

By learning schema

By learning objectives

By model modality

DL and its recent advances

The limitation and security concern

Hallucination

Privacy leakage

Intellectual property ownership

Bias, fairness, and their social impact

Adversarial attacks

Summary

5

AI Problems and Methods

Supervised learning methods

Logistic regression

Random forest

Support Vector Machines (SVM)

Neural networks

Deep learning

Convolutional neural networks

Unsupervised learning methods

K-means

t-SNE

Semi-supervised learning methods

Label propagation

Detecting anomalies

Isolation Forest

Summary

References

6

Workflow, Tools, and Libraries in AI Projects

Workflow of AI projects

Fundamental workflow – creating an AI model from scratch

Advanced topics– integrating an AI model into a product

Developing and creating a virtual environment

Tools and libraries for visual network traffic analysis

Background of visual network traffic analysis

Tools and libraries for malware detection

Background of malware detection

Summary

References

Part 3: Applications of AI in Cybersecurity

7

Malware and Network Intrusion Detection and Analysis

Technical requirements

Overcoming traditional difficulties

Proper datasets for creating an AI model

Malware analysis

Network intrusion detection

Exercise 1 – malware detection

Exercise 2 – network intrusion detection

Moving from detection to classification

Summary

8

User and Entity Behavior Analysis

Technical requirements

Shortcomings of traditional tools

Leveraging AI for UEBA

UEBA features

Feature extraction

Exercise – UEBA anomaly detection

Other use cases

Summary

9

Fraud, Spam, and Phishing Detection

Introducing fraud, phishing, and spam detection methods

Fraud detection

Phishing detection

Spam detection

Understanding phishing detection with a practical example

Introducing the collaborative anomaly detection

Summary

References

10

User Authentication and Access Control

Understanding user authentication and access control

User authentication

Multi-factor authentication

Authentication technologies

Access control

Exemplifying the user authentication and access control

OAuth2.0 and user authentication

SELinux and access control

Practicing user authentication and access control with Python

Using OAuth2.0 in mobile application authentication

Writing SELinux in Python to control the Ubuntu files

AI for user authentication – face recognition

Summary

11

Threat Intelligence

Technical requirements

Understanding threat intelligence

Working with AI for threat intelligence

Topic modeling

Exercise – extracting CTI information from X data

Data preprocessing

Building the model

Expanding on use cases of AI in threat intelligence

Summary

References

12

Anomaly Detection in Industrial Control Systems

Introducing the ICS and its components

Cyberattacks on ICSs

Cyberattacks on the ICS

Cyberattacks on the components of ICSs

Detecting anomaly behaviors in ICSs

Classification

Use cases and applications

Anomaly detection for the ICS

Ransomware detection for the ICS and its components

Challenges and future works

Summary

References

13

Large Language Models and Cybersecurity

From traditional methods to LLMs

Transformers

Large Language Models (LLMs)

Prompting

Retrieval augmented generation

Using LLMs for security

LLMs for vulnerability discovery

LLMs for threat intelligence

LLMs for spam and phishing detection

LLMs for a security operation center

LLMs for offensive security

The security of LLMs

Summary

References

Part 4: Common Problems When Applying AI in Cybersecurity

14

Data Quality and its Usage in the AI and LLM Era

Data quality and its usage

Characteristics of a high-quality dataset

Uses of high-quality datasets in real life

Examples of good data quality in AI and LLMs

NLP

Computer vision

Data quality accidents

Writing Python code to practice good data quality

Example 1 – data cleansing with pandas

Example 2 – data validation

Example 3 – handling missing values

Summary

15

Correlation, Causation, Bias, and Variance

Technical requirements

Introducing the statistical foundation

Understanding correlation and causation

Correlation

Causation

Introducing bias and variance

Bias

Variance

Bias and variance in polynomial curve fitting

Managing bias and variance

Case studies and examples

Case study 1 – correlation versus causation in phishing attacks

Case study 2 – managing bias and variance in IDS

Conclusion of case studies

Practical applications

Diagnostic tools for correlation and causation

Techniques to manage bias and variance

Advanced statistical techniques for enhanced security

Implementing responsible AI in cybersecurity

Summary

16

Evaluation, Monitoring, and Feedback Loop

Technical requirements

Evaluating models

Loss functions

Model metrics

Monitoring models

Monitoring during training

Monitoring during testing or production

Model monitoring tools

Human in the loop

Active learning

Summary

References

17

Learning in a Changing and Adversarial Environment

Technical requirements

Introduction to AML

The realistic learning environment

Arms race problem

Learning process with data flow

Adversarial threat modeling

Attacker model

Adversarial attack taxonomy

Transferability of adversarial samples

Defensive mechanisms

Defense as prevention

Defense as detection

Defense as a response

Practical tools for testing on adversarial attacks

Summary

References

18

Privacy, Accountability, Explainability, and Trust – Responsible AI

Technical requirements

Understanding the AI issues

Current challenges in AI security

Safety concerns

Significance of AI security and safety

Impact on individual privacy

Implications for national and global security

Ethical considerations and public trust

Addressing AI security and safety challenges

Theoretical approaches

Research development

Guidelines and standards

Tools and technologies

AI risk management framework

Main components of the AI risk management framework

Utilizing the framework in organizations

Preparing for the future

Summary

Part 5: Final Remarks and Takeaways

19

Summary

Summarizing what we’ve learned

Connecting chapters

Successes of AI in cybersecurity

Where to go from here

Open source projects and libraries

Other web links

Index

Other Books You May Enjoy

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

Chapter 1

, Big Data in Cybersecurity, 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.

Chapter 2

, Automation in Cybersecurity, 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.

Chapter 3

, Cybersecurity Data Analytics, 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.

Chapter 4

, AI, Machine Learning, and Statistics - A Taxonomy, 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.

Chapter 5

, AI Problems and Methods, 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.

Chapter 6

, Workflow, Tools, and Libraries in AI Projects, 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.

Chapter 7

, Malware and Network Intrusion Detection and Analysis, 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.

Chapter 8

, User and Entity Behavior Analysis, 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.

Chapter 9

, Fraud, Spam, and Phishing Detection, 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.

Chapter 10

, User Authentication and Access Control, 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.

Chapter 11

, Threat Intelligence, 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.

Chapter 12

, Anomaly Detection in Industrial Control Systems, 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.

Chapter 13

, Large Language Models and Cybersecurity, 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.

Chapter 14

, Data Quality and its Usage in the AI and LLM Era, 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.

Chapter 15

, Correlation, Causation, Bias, and Variance, 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.

Chapter 16

, Evaluation, Monitoring, and Feedback Loop, 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.

Chapter 17

, Learning in a Changing and Adversarial Environment, 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.

Chapter 18

, Privacy, Accountability, Explainability, and Trust – Responsible AI, 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 context.

Chapter 19

, Summary, contains a retrospective on what you have learned in previous chapters and helps you structure the knowledge you obtained while reading the book. Furthermore, it gives you some propositions for the next steps to enhance your knowledge and skills.

To get the most out of this book

To use this book in an optimal way, it’s important to go through the introductory and theoretical chapters to get a strong basis and use this basis in the further practical chapters with hands-on exercises. This way, you can get a well-rounded knowledge that can help in a wide range of scenarios.

The technical requirements are different and described at the beginning of each chapter, but the exercises are generally done in Python 3 using various Python libraries.

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    shuffle_files=True, as_supervised=True, with_info=True,

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Part 1: Data-Driven Cybersecurity and AI

This part introduces how big data technology and AI are changing how we solve problems in cybersecurity. It describes the role of data and automation, as well as the opportunities that collecting large-scale data brings. Furthermore, it enumerates the cybersecurity tools and approaches where big data analytics is already making a difference.

This part has the following chapters:

Chapter 1

, Big Data in Cybersecurity

Chapter 2

, Automation in Cybersecurity

Chapter 3

, Cybersecurity Data Analytics

1

Big Data in Cybersecurity

In this chapter, we will explore the significance of big data in cybersecurity. More precisely, it will encompass an overview of challenges, applications, and technologies associated with big data in cybersecurity, along with considerations related to privacy and ethics. Whether you are new to the concept of big data in cybersecurity or seeking to deepen your understanding, this chapter will provide valuable insights and detailed information.

In this chapter, we’re going to cover the following main topics:

What is big data?

Big data challenges in cybersecurity

Big data applications in cybersecurity

Big data technologies for cybersecurity

By the end of this chapter, you will have gained a comprehensive understanding of how big data is reshaping the landscape of cybersecurity. From grasping the fundamental concept of big data and its distinctions from conventional data processing to navigating the intricate challenges it presents in cybersecurity, you will develop a solid foundation. You’ll explore diverse applications that harness the power of big data for threat detection, fraud prevention, and incident response (IR), gaining insights into the cutting-edge technologies driving these advancements. Through real-world use cases, you’ll witness the tangible impact of big data in enhancing cyber resilience. Additionally, you’ll be equipped to address critical ethical and privacy considerations inherent in using extensive datasets for security purposes, ensuring a well-rounded perspective on this transformative field.

Technical requirements

There are no specific technical prerequisites for delving into this chapter, apart from a basic understanding of computer science concepts. Whether you’re a cybersecurity enthusiast looking to explore the broader implications of big data or a professional seeking to deepen your understanding of its applications, this chapter is designed to be accessible to a wide range of readers. It offers insights and explanations in a clear and approachable manner, making the content valuable for both technical and non-technical individuals interested in the intersection of big data and cybersecurity.

What is big data?

Before delving into the introduction of big data, it is essential to understand the concept of data. Data processed by a computer comprises quantities, characters, or symbols, which can be stored, transmitted, and recorded as electrical signals on magnetic, optical, or mechanical media. Big data, on the other hand, refers to an extensive collection of data that is massive in volume and continues to grow exponentially over time. It is characterized by its substantial size and complexity, to the extent that traditional data management tools cannot efficiently store and process it. Big data is a unique form of data that presents immense challenges and opportunities due to its sheer magnitude. Let’s now explore these distinctive features, or the four Vs of big data, in detail:

Volume: Big data refers to vast amounts of data generated, collected, and stored by various sources, including sensors, social media, transactional data, and more. The sheer volume of data is one of the defining characteristics of big data.

Velocity: Big data is generated and processed at an unprecedented rate. Data can be developed in real time or near real time from various sources. The speed at which data is produced and needs to be processed is a crucial characteristic of big data. This poses challenges in capturing, storing, and processing data in real time or near real time.

Variety: Big data comes in various formats and types, including structured, unstructured, and semi-structured data. Structured data contains data that can be organized in a traditional format, such as spreadsheets or databases. Unstructured data comprises data that comes with no specific format, such as text, images, audio, and video data. Semi-structured data falls in between, having some structure but not fully organized. The diverse nature of data types and formats is another characteristic of big data.

Veracity: Big data can be noisy and uncertain, with varying data quality and accuracy levels. Data may be incomplete, inconsistent, or contain errors, impacting the reliability of insights and analysis derived from big data. Ensuring data veracity, including data quality, accuracy, and reliability, is a critical characteristic of big data.

Big data has become increasingly important in various domains due to its potential to unlock insights, drive innovation, and create value. In today’s data-driven world, organizations across different industries leverage big data to gain deeper insights, make informed decisions, and optimize processes. From business and commerce to healthcare, finance, transportation and logistics, smart cities, social sciences, and cybersecurity, big data transforms how these domains operate and deliver value to their stakeholders. With the ability to capture, store, process, and analyze vast amounts of data, big data analytics empowers organizations to extract meaningful information, identify patterns, and make data-driven decisions, leading to improved outcomes, increased efficiency, and competitive advantage:

Business and commerce: Big data transforms how businesses operate, enabling organizations to gain deeper insights into customer behavior, market trends, and operational efficiency. Through big data analytics, companies can make data-driven decisions, optimize processes, improve customer experience, and gain a competitive advantage.

Finance: Big data plays a crucial role in the finance industry, where vast amounts of data are generated and analyzed for risk assessment, fraud detection, algorithmic trading, and customer profiling. Big data analytics helps financial institutions gain insights into market trends, customer behavior, and risk management, leading to improved decision-making and financial performance.

Healthcare: Big data revolutionizes healthcare by enabling data-driven decision-making, personalized medicine, and predictive analytics. Analyzing large and complex healthcare datasets, including electronic health records (EHRs), medical imaging data, and genomics data, can help in disease prediction, early detection, treatment planning, and patient care optimization.

Transportation and logistics: Big data alters the transportation and logistics industry by optimizing supply chain operations, improving transportation efficiency, and enhancing safety. Real-time data from sensors, telematics, and other sources can be analyzed to optimize routes, reduce fuel consumption, enhance vehicle maintenance, and improve overall operational efficiency.

Smart cities: Big data is being used to create smart cities by integrating data from various sources, such as sensors, social media, and public records, to improve urban planning, transportation, energy management, and public safety. Big data analytics helps make cities more efficient, sustainable, and resilient, leading to improved quality of life for citizens.

Social sciences: Big data is increasingly used in social sciences to analyze large-scale social data, such as social media data, survey data, and public records, to understand human behavior, social dynamics, and societal trends. Big data analytics in social sciences can help in political science, economics, sociology, and psychology, leading to better policy-making and decision-making.

Cybersecurity: Big data plays a critical role in cybersecurity by analyzing large volumes of data from various sources, such as logs, network traffic, and user behavior, to detect and mitigate cyber threats. Advanced analytics techniques, such as machine learning (ML) and anomaly detection, applied to big data can help identify patterns, detect anomalies, and prevent cyber-attacks. We’ll delve into further details about big data and cybersecurity in the remainder of this chapter.

In summary, big data has become a crucial asset in various domains, offering the potential to unlock valuable insights, drive innovation, and create value. The ability to harness and analyze large and complex datasets is transforming industries, leading to improved decision-making, enhanced operational efficiency, and better outcomes in business, healthcare, finance, transportation and logistics, smart cities, social sciences, and cybersecurity.

At this point, the concept of big data should be comprehensible to