Mastering Machine Learning for Penetration Testing · Develop an Extensive Skill Set to Break Self-Learning Systems Using Python by Chebbi, Chiheb -- Read -- Imperial Library of Trantor

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Title Page Copyright and Credits

Mastering Machine Learning for Penetration Testing

Dedication Packt Upsell

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Contributors

About the author About the reviewer Packt is searching for authors like you

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

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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

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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