Machine Learning for Cybersecurity Cookbook : Over 80 Recipes on How to Implement Machine Learning Algorithms for Building Security Systems Using Python
This book helps data scientists and cybersecurity experts on implementing the latest AI techniques in cybersecurity. Concrete and clear steps for implementing ML security systems are provided, saving you months in research and development. By the end of this book, you will be able to build defensive systems to curb cybersecurity threats
1 online resource (338 pages)
9781838556341, 1838556346
1129196943
Cover
Title Page
Copyright and Credits
About Packt
Contributors
Table of Contents
Preface
Chapter 1: Machine Learning for Cybersecurity
Technical requirements
Train-test-splitting your data
Getting ready
How to do it ..
How it works ..
Standardizing your data
Getting ready
How to do it ..
How it works ..
Summarizing large data using principal component analysis
Getting ready
How to do it ..
How it works ..
Generating text using Markov chains
Getting ready
How to do it ..
How it works ... Performing clustering using scikit-learn
Getting ready
How to do it ..
How it works ..
Training an XGBoost classifier
Getting ready
How to do it ..
How it works ..
Analyzing time series using statsmodels
Getting ready
How to do it ..
How it works ..
Anomaly detection with Isolation Forest
Getting ready
How to do it ..
How it works ..
Natural language processing using a hashing vectorizer and tf-idf with scikit-learn
Getting ready
How to do it ..
How it works ..
Hyperparameter tuning with scikit-optimize
Getting ready
How to do it ... How it works ..
Chapter 2: Machine Learning-Based Malware Detection
Technical requirements
Malware static analysis
Computing the hash of a sample
Getting ready
How to do it ..
How it works ..
YARA
Getting ready
How to do it ..
How it works ..
Examining the PE header
Getting ready
How to do it ..
How it works ..
Featurizing the PE header
Getting ready
How to do it ..
How it works ..
Malware dynamic analysis
Getting ready
How to do it ..
How it works ..
Using machine learning to detect the file type Scraping GitHub for files of a specific type
Getting ready
How to do it ..
How it works ..
Classifying files by type
Getting ready
How to do it ..
How it works ..
Measuring the similarity between two strings
Getting ready
How to do it ..
How it works ..
Measuring the similarity between two files
Getting ready
How to do it ..
How it works ..
Extracting N-grams
Getting ready
How to do it ..
How it works ..
Selecting the best N-grams
Getting ready
How to do it ..
How it works ..
Building a static malware detector
Getting ready How to do it ..
How it works ..
Tackling class imbalance
Getting ready
How to do it ..
How it works ..
Handling type I and type II errors
Getting ready
How to do it ..
How it works ..
Chapter 3: Advanced Malware Detection
Technical requirements
Detecting obfuscated JavaScript
Getting ready
How to do it ..
How it works ..
Featurizing PDF files
Getting ready
How to do it ..
How it works ..
Extracting N-grams quickly using the hash-gram algorithm
Getting ready
How to do it ..
How it works ..
See also
Building a dynamic malware classifier
Getting ready