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10 Machine Learning Blueprints You Should Know for Cybersecurity

By Rajvardhan Oak
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  1. Free Chapter
    Chapter 2: Detecting Suspicious Activity
About this book
Machine learning in security is harder than other domains because of the changing nature and abilities of adversaries, high stakes, and a lack of ground-truth data. This book will prepare machine learning practitioners to effectively handle tasks in the challenging yet exciting cybersecurity space. The book begins by helping you understand how advanced ML algorithms work and shows you practical examples of how they can be applied to security-specific problems with Python – by using open source datasets or instructing you to create your own. In one exercise, you’ll also use GPT 3.5, the secret sauce behind ChatGPT, to generate an artificial dataset of fabricated news. Later, you’ll find out how to apply the expert knowledge and human-in-the-loop decision-making that is necessary in the cybersecurity space. This book is designed to address the lack of proper resources available for individuals interested in transitioning into a data scientist role in cybersecurity. It concludes with case studies, interview questions, and blueprints for four projects that you can use to enhance your portfolio. By the end of this book, you’ll be able to apply machine learning algorithms to detect malware, fake news, deep fakes, and more, along with implementing privacy-preserving machine learning techniques such as differentially private ML.
Publication date:
May 2023
Publisher
Packt
ISBN
9781804619476

 

On Cybersecurity and Machine Learning

With the dawn of the Information Age, cybersecurity has become a pressing issue in today’s society and a skill that is much sought after in industry. Businesses, governments, and individual users are all at risk of security attacks and breaches. The fundamental goal of cybersecurity is to keep users and their data safe. Cybersecurity is a multi-faceted problem, ranging from highly technical domains (cryptography and network attacks) to user-facing domains (detecting hate speech or fraudulent credit card transactions). It helps to prevent sensitive information from being corrupted, avoid financial fraud and losses, and safeguard users and their devices from harmful actors.

A large part of cybersecurity analytics, investigations, and detections are now driven by machine learning (ML)and “smart” systems. Applying data science and ML to the security space presents a unique set of challenges: the lack of sufficiently labeled...

 

The basics of cybersecurity

This book aims to marry two important fields of research: cybersecurity and ML. We will present a brief overview of cybersecurity, how it is defined, what the end goals are, and what problems arise.

Traditional principles of cybersecurity

The fundamental aim of cybersecurity is to keep users and data safe. Traditionally, the goals of cybersecurity were three-fold: confidentiality, integrity, and availability, the CIA triad.

Let us now examine each of these in depth.

Confidentiality

The confidentiality goal aims to keep data secret from unauthorized parties. Only authorized entities should have access to data.

Confidentiality can be achieved by encrypting data. Encryption is a process where plain-text data is coded into a ciphertext using an encryption key. The ciphertext is not human-readable; a corresponding decryption key is needed to decode the data. Encryption of information being sent over networks prevents attackers from reading the...

 

An overview of machine learning

In this section, we will present a brief overview of ML principles and techniques. The traditional computing paradigm defines an algorithm as having three elements: the input, an output, and a process that specifies how to derive the output from the input. For example, in a credit card detection system, a module to flag suspicious transactions may have transaction metadata (location, amount, type) as input and the flag (suspicious or not) as output. The process will define the rule to set the flag based on the input, as shown in Figure 1.2:

Figure 1.2 – Traditional input-process-output model for fraud detection

Figure 1.2 – Traditional input-process-output model for fraud detection

ML is a drastic change to the input-process-output philosophy. The traditional approach defined computing as deriving the output by applying the process to the input. In ML, we are given the input and output, and the task is to derive the process that connects the two.

Continuing our analogy of the credit...

 

Machine learning – cybersecurity versus other domains

ML today is applied to a wide variety of domains, some of which are detailed in the following list:

  • In sales and marketing, to identify the segment of customers likely to buy a particular product
  • In online advertising, for click prediction and to display ads accordingly
  • In climate and weather forecasting, to predict trends based on centuries of data
  • In recommendation systems, to find the best items (movies, songs, posts, and people) relevant to a user

While every sector imaginable applies ML today, the nuances of it being applied to cybersecurity are different from other fields. In the following subsections, we will see some of the reasons why it is much more challenging to apply ML to the cybersecurity domain than to other domains such as sales or advertising.

High stakes

Security problems often involve making crucial decisions that can impact money, resources, and even life. A fraud detection...

 

Summary

This introductory chapter provided a brief overview of cybersecurity and ML. We studied the fundamental goals of traditional cybersecurity and how those goals have now evolved to capture other tasks such as fake news, deep fakes, click spam, and fraud. User privacy, a topic of growing importance in the world, was also introduced. On the ML side, we covered the basics from the ground up: beginning with how ML differs from traditional computing and moving on to the methods, approaches, and common terms used in ML. Finally, we also highlighted the key differences in ML for cybersecurity that make it so much more challenging than other fields. The coming chapters will focus on applying these concepts to designing and implementing ML models for security issues. In the next chapter, we will discuss how to detect anomalies and network attacks using ML.

About the Author
  • Rajvardhan Oak

    Rajvardhan Oak is a cybersecurity expert, researcher, and scientist with a focus on machine learning solutions to security issues such as fake news, malware, and botnets. He obtained his bachelor's degree from the University of Pune, India, and his master's degree from the University of California, Berkeley. He has served on the editorial committees of multiple technical conferences and journals. His work has been featured by prominent news outlets such as WIRED magazine and the Daily Mail. In 2022, he received the ISC2 Global Achievement Award for Excellence in Cybersecurity. He is based in the Seattle area and works for Microsoft as an applied scientist in the ads fraud division.

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10 Machine Learning Blueprints You Should Know for Cybersecurity
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