更新时间：2021-06-25 21:03:42
封面
版权信息
Dedication
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Contributors
About the author
About the reviewer
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Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Introduction to Machine Learning in Pentesting
Technical requirements
Artificial intelligence and machine learning
Machine learning models and algorithms
Supervised
Bayesian classifiers
Support vector machines
Decision trees
Semi-supervised
Unsupervised
Artificial neural networks
Linear regression
Logistic regression
Clustering with k-means
Reinforcement
Performance evaluation
Dimensionality reduction
Improving classification with ensemble learning
Machine learning development environments and Python libraries
NumPy
SciPy
TensorFlow
Keras
pandas
Matplotlib
scikit-learn
NLTK
Theano
Machine learning in penetration testing - promises and challenges
Deep Exploit
Summary
Questions
Further reading
Phishing Domain Detection
Social engineering overview
Social Engineering Engagement Framework
Steps of social engineering penetration testing
Building real-time phishing attack detectors using different machine learning models
Phishing detection with logistic regression
Phishing detection with decision trees
NLP in-depth overview
Open source NLP libraries
Spam detection with NLTK
Malware Detection with API Calls and PE Headers
Malware overview
Malware analysis
Static malware analysis
Dynamic malware analysis
Memory malware analysis
Evasion techniques
Portable Executable format files
Machine learning malware detection using PE headers
Machine learning malware detection using API calls
Malware Detection with Deep Learning
Artificial neural network overview
Implementing neural networks in Python
Deep learning model using PE headers
Deep learning model with convolutional neural networks and malware visualization
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short Term Memory networks
Hopfield networks
Boltzmann machine networks
Malware detection with CNNs
Promises and challenges in applying deep learning to malware detection
Botnet Detection with Machine Learning
Botnet overview
Building a botnet detector model with multiple machine learning techniques
How to build a Twitter bot detector
