Mastering machine learning for penetration testing : develop an extensive skill set to break self-learning systems using Python / Chiheb Chebbi.

Format:

Book

Author/Creator:

Chebbi, Chiheb, author.

Language:

English

Subjects (All):

Python (Computer program language).

Penetration testing (Computer security).

Physical Description:

1 online resource (264 pages)

Edition:

1st edition

Place of Publication:

Birmingham : Packt, 2018.

System Details:

text file

Summary:

Become a master at penetration testing using machine learning with Python About This Book Identify ambiguities and breach intelligent security systems Perform unique cyber attacks to breach robust systems Learn to leverage machine learning algorithms Who This Book Is For This book is for pen testers and security professionals who are interested in learning techniques to break an intelligent security system. Basic knowledge of Python is needed, but no prior knowledge of machine learning is necessary. What You Will Learn Take an in-depth look at machine learning Get to know natural language processing (NLP) Understand malware feature engineering Build generative adversarial networks using Python libraries Work on threat hunting with machine learning and the ELK stack Explore the best practices for machine learning In Detail Cyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it's important for pentesters and security researchers to understand how these systems work, and to breach them for testing purposes. This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you've gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you'll see how to find loopholes and surpass a self-learning security system. As you make your way through the chapters, you'll focus on topics such as network intrusion detection and AV and IDS evasion. We'll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system. By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system. Style and approach This book takes a step-by-step approach to identify the loop holes in a self-learning security system. You will be able to efficiently breach a machine learning system with the help of best practices towards the end of the book.

Contents:

Cover

Title Page

Copyright and Credits

Dedication

Packt Upsell

Contributors

Table of Contents

Preface

Chapter 1: 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

Chapter 2: 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

Chapter 3: 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

Chapter 4: 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

Chapter 5: Botnet Detection with Machine Learning

Botnet overview

Building a botnet detector model with multiple machine learning techniques

How to build a Twitter bot detector

Visualization with seaborn

Chapter 6: Machine Learning in Anomaly Detection Systems

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

Chapter 7: Detecting Advanced Persistent Threats

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

Chapter 8: Evading Intrusion Detection Systems

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

Chapter 9: Bypassing Machine Learning Malware Detectors

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

Chapter 10: Best Practices for Machine Learning and Feature Engineering

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

Assessments

Other Books You May Enjoy

Index.

Notes:

Includes bibliographical references.

Description based on print version record.

ISBN:

9781788993111

178899311X

OCLC:

1043629671