Mastering Machine Learning for Penetration Testing
Chiheb Chebbi更新时间：2021-06-25 21:03:42
最新章节：Leave a review - let other readers know what you think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
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Conventions used
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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
Technical requirements
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
Summary
Questions
Malware Detection with API Calls and PE Headers
Technical requirements
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
Summary
Questions
Further reading
Malware Detection with Deep Learning
Technical requirements
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
Summary
Questions
Further reading
Botnet Detection with Machine Learning
Technical requirements
Botnet overview
Building a botnet detector model with multiple machine learning techniques
How to build a Twitter bot detector
Visualization with seaborn
Summary
Questions
Further reading
Machine Learning in Anomaly Detection Systems
Technical requirements
An overview of anomaly detection techniques
Static rules technique
Network attacks taxonomy
The detection of network anomalies
HIDS
NIDS
Anomaly-based IDS
Building your own IDS
The Kale stack
Summary
Questions
Further reading
Detecting Advanced Persistent Threats
Technical requirements
Threats and risk analysis
Threat-hunting methodology
The cyber kill chain
The diamond model of intrusion analysis
Threat hunting with the ELK Stack
Elasticsearch
Kibana
Logstash
Machine learning with the ELK Stack using the X-Pack plugin
Summary
Questions
Evading Intrusion Detection Systems
Technical requirements
Adversarial machine learning algorithms
Overfitting and underfitting
Overfitting and underfitting with Python
Detecting overfitting
Adversarial machine learning
Evasion attacks
Poisoning attacks
Adversarial clustering
Adversarial features
CleverHans
The AML library
EvadeML-Zoo
Evading intrusion detection systems with adversarial network systems
Summary
Questions
Further reading
Bypassing Machine Learning Malware Detectors
Technical requirements
Adversarial deep learning
Foolbox
Deep-pwning
EvadeML
Bypassing next generation malware detectors with generative adversarial networks
The generator
The discriminator
MalGAN
Bypassing machine learning with reinforcement learning
Reinforcement learning
Summary
Questions
Further reading
Best Practices for Machine Learning and Feature Engineering
Technical requirements
Feature engineering in machine learning
Feature selection algorithms
Filter methods
Pearson's correlation
Linear discriminant analysis
Analysis of variance
Chi-square
Wrapper methods
Forward selection
Backward elimination
Recursive feature elimination
Embedded methods
Lasso linear regression L1
Ridge regression L2
Tree-based feature selection
Best practices for machine learning
Information security datasets
Project Jupyter
Speed up training with GPUs
Selecting models and learning curves
Machine learning architecture
Coding
Data handling
Business contexts
Summary
Questions
Further reading
Assessments
Chapter 1 – Introduction to Machine Learning in Pentesting
Chapter 2 – Phishing Domain Detection
Chapter 3 – Malware Detection with API Calls and PE Headers
Chapter 4 – Malware Detection with Deep Learning
Chapter 5 – Botnet Detection with Machine Learning
Chapter 6 – Machine Learning in Anomaly Detection Systems
Chapter 7 – Detecting Advanced Persistent Threats
Chapter 8 – Evading Intrusion Detection Systems with Adversarial Machine Learning
Chapter 9 – Bypass Machine Learning Malware Detectors
Chapter 10 – Best Practices for Machine Learning and Feature Engineering
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更新时间：2021-06-25 21:03:42