Book description
Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection
Key Features
- Manage data of varying complexity to protect your system using the Python ecosystem
- Apply ML to pentesting, malware, data privacy, intrusion detection system(IDS) and social engineering
- Automate your daily workflow by addressing various security challenges using the recipes covered in the book
Book Description
Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you'll learn how to use Python libraries such as TensorFlow and scikit-learn to implement the latest artificial intelligence (AI) techniques and handle challenges faced by cybersecurity researchers.
You'll begin by exploring various machine learning (ML) techniques and tips for setting up a secure lab environment. Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for malware, which you'll train and test on real samples. As you progress, you'll build self-learning, reliant systems to handle cybersecurity tasks such as identifying malicious URLs, spam email detection, intrusion detection, network protection, and tracking user and process behavior. Later, you'll apply generative adversarial networks (GANs) and autoencoders to advanced security tasks. Finally, you'll delve into secure and private AI to protect the privacy rights of consumers using your ML models.
By the end of this book, you'll have the skills you need to tackle real-world problems faced in the cybersecurity domain using a recipe-based approach.
What you will learn
- Learn how to build malware classifiers to detect suspicious activities
- Apply ML to generate custom malware to pentest your security
- Use ML algorithms with complex datasets to implement cybersecurity concepts
- Create neural networks to identify fake videos and images
- Secure your organization from one of the most popular threats - insider threats
- Defend against zero-day threats by constructing an anomaly detection system
- Detect web vulnerabilities effectively by combining Metasploit and ML
- Understand how to train a model without exposing the training data
Who this book is for
This book is for cybersecurity professionals and security researchers who are looking to implement the latest machine learning techniques to boost computer security, and gain insights into securing an organization using red and blue team ML. This recipe-based book will also be useful for data scientists and machine learning developers who want to experiment with smart techniques in the cybersecurity domain. Working knowledge of Python programming and familiarity with cybersecurity fundamentals will help you get the most out of this book.
Table of contents
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
-
Machine Learning for Cybersecurity
- Technical requirements
- Train-test-splitting your data
- Standardizing your data
- Summarizing large data using principal component analysis
- Generating text using Markov chains
- Performing clustering using scikit-learn
- Training an XGBoost classifier
- Analyzing time series using statsmodels
- Anomaly detection with Isolation Forest
- Natural language processing using a hashing vectorizer and tf-idf with scikit-learn
- Hyperparameter tuning with scikit-optimize
-
Machine Learning-Based Malware Detection
- Technical requirements
- Malware static analysis
- Malware dynamic analysis
- Using machine learning to detect the file type
- Measuring the similarity between two strings
- Measuring the similarity between two files
- Extracting N-grams
- Selecting the best N-grams
- Building a static malware detector
- Tackling class imbalance
- Handling type I and type II errors
-
Advanced Malware Detection
- Technical requirements
- Detecting obfuscated JavaScript
- Featurizing PDF files
- Extracting N-grams quickly using the hash-gram algorithm
- Building a dynamic malware classifier
- MalConv – end-to-end deep learning for malicious PE detection
- Tackling packed malware
- MalGAN – creating evasive malware
- Tracking malware drift
- Machine Learning for Social Engineering
-
Penetration Testing Using Machine Learning
- Technical requirements
- CAPTCHA breaker
- Neural network-assisted fuzzing
- DeepExploit
- Web server vulnerability scanner using machine learning (GyoiThon)
- Deanonymizing Tor using machine learning
- IoT device type identification using machine learning
- Keystroke dynamics
- Malicious URL detector
- Deep-pwning
- Deep learning-based system for the automatic detection of software vulnerabilities
-
Automatic Intrusion Detection
- Technical requirements
- Spam filtering using machine learning
- Phishing URL detection
- Capturing network traffic
- Network behavior anomaly detection
- Botnet traffic detection
- Insider threat detection
- Detecting DDoS
- Credit card fraud detection
- Counterfeit bank note detection
- Ad blocking using machine learning
- Wireless indoor localization
- Securing and Attacking Data with Machine Learning
- Secure and Private AI
- Appendix
- Other Books You May Enjoy
Product information
- Title: Machine Learning for Cybersecurity Cookbook
- Author(s):
- Release date: November 2019
- Publisher(s): Packt Publishing
- ISBN: 9781789614671
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