Cargando…
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited,
2019.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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