Work on 10 practical projects, each with a blueprint for a different machine learning technique, and apply them in the real world to fight against cybercrime
Key Features
Learn how to frame a cyber security problem as a machine learning problem
Examine your model for robustness against adversarial machine learning
Build your portfolio, enhance your resume, and ace interviews to become a cybersecurity data scientist
Book Description
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.
What you will learn
Use GNNs to build feature-rich graphs for bot detection and engineer graph-powered embeddings and features
Discover how to apply ML techniques in the cybersecurity domain
Apply state-of-the-art algorithms such as transformers and GNNs to solve security-related issues
Leverage ML to solve modern security issues such as deep fake detection, machine-generated text identification, and stylometric analysis
Apply privacy-preserving ML techniques and use differential privacy to protect user data while training ML models
Build your own portfolio with end-to-end ML projects for cybersecurity
Who this book is for
This book is for machine learning practitioners interested in applying their skills to solve cybersecurity issues. Cybersecurity workers looking to leverage ML methods will also find this book useful. An understanding of the fundamental machine learning concepts and beginner-level knowledge of Python programming are needed to grasp the concepts in this book. Whether you're a beginner or an experienced professional, this book offers a unique and valuable learning experience that'll help you develop the skills needed to protect your network and data against the ever-evolving threat landscape.
Author(s): Rajvardhan Oak
Edition: 1
Publisher: Packt Publishing
Year: 2023
Language: English
Pages: 330
10 Machine Learning Blueprints You Should Know for Cybersecurity
Contributors
About the author
About the reviewers
Preface
Who this book is for
What this book covers
To get the most out of this book
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Conventions used
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Chapter 1: On Cybersecurity and Machine Learning
The basics of cybersecurity
Traditional principles of cybersecurity
Modern cybersecurity – a multi-faceted issue
Privacy
An overview of machine learning
Machine learning workflow
Supervised learning
Unsupervised learning
Semi-supervised learning
Evaluation metrics
Machine learning – cybersecurity versus other domains
Summary
Chapter 2: Detecting Suspicious Activity
Technical requirements
Basics of anomaly detection
What is anomaly detection?
Introducing the NSL-KDD dataset
Statistical algorithms for intrusion detection
Univariate outlier detection
Elliptic envelope
Local outlier factor
Machine learning algorithms for intrusion detection
Density-based scan (DBSCAN)
One-class SVM
Isolation forest
Autoencoders
Summary
Chapter 3: Malware Detection Using Transformers and BERT
Technical requirements
Basics of malware
What is malware?
Types of malware
Malware detection
Malware detection methods
Malware analysis
Transformers and attention
Understanding attention
Understanding transformers
Understanding BERT
Detecting malware with BERT
Malware as language
The relevance of BERT
Getting the data
Preprocessing the data
Building a classifier
Summary
Chapter 4: Detecting Fake Reviews
Technical requirements
Reviews and integrity
Why fake reviews exist
Evolution of fake reviews
Statistical analysis
Exploratory data analysis
Feature extraction
Statistical tests
Modeling fake reviews with regression
Ordinary Least Squares regression
OLS assumptions
Interpreting OLS regression
Implementing OLS regression
Summary
Chapter 5: Detecting Deepfakes
Technical requirements
All about deepfakes
A foray into GANs
How are deepfakes created?
The social impact of deepfakes
Detecting fake images
A naive model to detect fake images
Detecting deepfake videos
Building deepfake detectors
Summary
Chapter 6: Detecting Machine-Generated Text
Technical requirements
Text generation models
Understanding GPT
Naïve detection
Creating the dataset
Feature exploration
Using machine learning models for detecting text
Playing around with the model
Automatic feature extraction
Transformer methods for detecting automated text
Compare and contrast
Summary
Chapter 7: Attributing Authorship and How to Evade It
Technical requirements
Authorship attribution and obfuscation
What is authorship attribution?
What is authorship obfuscation?
Techniques for authorship attribution
Dataset
Feature extraction
Training the attributor
Improving authorship attribution
Techniques for authorship obfuscation
Improving obfuscation techniques
Summary
Chapter 8: Detecting Fake News with Graph Neural Networks
Technical requirements
An introduction to graphs
What is a graph?
Representing graphs
Graphs in the real world
Machine learning on graphs
Traditional graph learning
Graph embeddings
GNNs
Fake news detection with GNN
Modeling a GNN
The UPFD framework
Dataset and setup
Implementing GNN-based fake news detection
Playing around with the model
Summary
Chapter 9: Attacking Models with Adversarial Machine Learning
Technical requirements
Introduction to AML
The importance of ML
Adversarial attacks
Adversarial tactics
Attacking image models
FGSM
PGD
Attacking text models
Manipulating text
Further attacks
Developing robustness against adversarial attacks
Adversarial training
Defensive distillation
Gradient regularization
Input preprocessing
Ensemble methods
Certified defenses
Summary
Chapter 10: Protecting User Privacy with Differential Privacy
Technical requirements
The basics of privacy
Core elements of data privacy
Privacy and the GDPR
Privacy by design
Privacy and machine learning
Differential privacy
What is differential privacy?
Differential privacy – a real-world example
Benefits of differential privacy
Differentially private machine learning
IBM Diffprivlib
Credit card fraud detection with differential privacy
Differentially private deep learning
DP-SGD algorithm
Implementation
Differential privacy in practice
Summary
Chapter 11: Protecting User Privacy with Federated Machine Learning
Technical requirements
An introduction to federated machine learning
Privacy challenges in machine learning
How federated machine learning works
The benefits of federated learning
Challenges in federated learning
Implementing federated averaging
Importing libraries
Dataset setup
Client setup
Model implementation
Weight scaling
Global model initialization
Setting up the experiment
Putting it all together
Reviewing the privacy-utility trade-off in federated learning
Global model (no privacy)
Local model (full privacy)
Understanding the trade-off
Beyond the MNIST dataset
Summary
Chapter 12: Breaking into the Sec-ML Industry
Study guide for machine learning and cybersecurity
Machine learning theory
Hands-on machine learning
Cybersecurity
Interview questions
Theory-based questions
Experience-based questions
Conceptual questions
Additional project blueprints
Improved intrusion detection
Adversarial attacks on intrusion detection
Hate speech and toxicity detection
Detecting fake news and misinformation
Summary
Index
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