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Hands-On Machine Learning for Cybersecurity

By : Halder, Sinan Ozdemir

2.7 (6)

Hands-On Machine Learning for Cybersecurity

2.7 (6)

By: Halder, Sinan Ozdemir

Overview of this book

Cyber threats today are one of the costliest losses that an organization can face. In this book, we use the most efficient tool to solve the big problems that exist in the cybersecurity domain. The book begins by giving you the basics of ML in cybersecurity using Python and its libraries. You will explore various ML domains (such as time series analysis and ensemble modeling) to get your foundations right. You will implement various examples such as building system to identify malicious URLs, and building a program to detect fraudulent emails and spam. Later, you will learn how to make effective use of K-means algorithm to develop a solution to detect and alert you to any malicious activity in the network. Also learn how to implement biometrics and fingerprint to validate whether the user is a legitimate user or not. Finally, you will see how we change the game with TensorFlow and learn how deep learning is effective for creating models and training systems

Preface

Preface

Who this book is for

What this book covers

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Free Chapter

Basics of Machine Learning in Cybersecurity

Basics of Machine Learning in Cybersecurity

What is machine learning?

Summary

Time Series Analysis and Ensemble Modeling

Time Series Analysis and Ensemble Modeling

What is a time series?

Classes of time series models

Time series decomposition

Use cases for time series

Time series analysis in cybersecurity

Time series trends and seasonal spikes

Predicting DDoS attacks

Ensemble learning methods

Voting ensemble method to detect cyber attacks

Summary

Segregating Legitimate and Lousy URLs

Segregating Legitimate and Lousy URLs

Introduction to the types of abnormalities in URLs

Using heuristics to detect malicious pages

Using machine learning to detect malicious URLs

Logistic regression to detect malicious URLs

SVM to detect malicious URLs

Multiclass classification for URL classification

Summary

Knocking Down CAPTCHAs

Knocking Down CAPTCHAs

Characteristics of CAPTCHA

Using artificial intelligence to crack CAPTCHA

Summary

Using Data Science to Catch Email Fraud and Spam

Using Data Science to Catch Email Fraud and Spam

Email spoofing

Spam detection

Summary

Efficient Network Anomaly Detection Using k-means

Efficient Network Anomaly Detection Using k-means

Stages of a network attack

Dealing with lateral movement in networks

Using Windows event logs to detect network anomalies

Ingesting active directory data

Data parsing

Modeling

Detecting anomalies in a network with k-means

Summary

Decision Tree and Context-Based Malicious Event Detection

Decision Tree and Context-Based Malicious Event Detection

Adware

Bots

Bugs

Ransomware

Rootkit

Spyware

Trojan horses

Viruses

Worms

Malicious data injection within databases

Malicious injections in wireless sensors

Use case

Revisiting malicious URL detection with decision trees

Summary

Catching Impersonators and Hackers Red Handed

Catching Impersonators and Hackers Red Handed

Understanding impersonation

Different types of impersonation fraud

Levenshtein distance

Summary

Changing the Game with TensorFlow

Changing the Game with TensorFlow

Introduction to TensorFlow

Installation of TensorFlow

TensorFlow for Windows users

Hello world in TensorFlow

Importing the MNIST dataset

Computation graphs

Tensor processing unit

Using TensorFlow for intrusion detection

Summary

Financial Fraud and How Deep Learning Can Mitigate It

Financial Fraud and How Deep Learning Can Mitigate It

Machine learning to detect financial fraud

Logistic regression classifier – under-sampled data

Deep learning time

Summary

Case Studies

Case Studies

Introduction to our password dataset

Summary

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Spam detection

We will now deal with a hands-on exercise of separating spam emails a set of non-spam, or ham, emails. Unlike manual spam detectors, where users mark email as spam upon manual verification, this method uses machine learning to distinguish between spam and ham emails. The stages of detection can be illustrated as follows:

Types of mail servers

Mail servers are meant to receive email items, and they consist of a return path. The path bounces an email off to the ID mentioned in the return path. Mail servers are equivalent to the neighborhood mailman. All emails pass through a series of servers called mail-servers through series of processes.

The different types of mail servers are as follows:

POP3 email servers...

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