Book Image

Hands-On Machine Learning for Cybersecurity

By : Soma Halder, Sinan Ozdemir
Book Image

Hands-On Machine Learning for Cybersecurity

By: Soma 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

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Table of Contents (13 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Basics of Machine Learning in Cybersecurity
Basics of Machine Learning in Cybersecurity
What is machine learning?
Summary
2
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
3
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
4
Knocking Down CAPTCHAs
Knocking Down CAPTCHAs
Characteristics of CAPTCHA
Using artificial intelligence to crack CAPTCHA
Summary
5
Using Data Science to Catch Email Fraud and Spam
Using Data Science to Catch Email Fraud and Spam
Email spoofing
Spam detection
Summary
6
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
7
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
8
Catching Impersonators and Hackers Red Handed
Catching Impersonators and Hackers Red Handed
Understanding impersonation
Different types of impersonation fraud
Levenshtein distance
Summary
9
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
10
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
11
Case Studies
Case Studies
Introduction to our password dataset
Summary
12
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Levenshtein distance

Levenshtein distance is an editing distance-based metric that helps to detect the distance between two alphanumeric string sequences. It computes the number of edits (replacements or insertions) required to traverse from the first character sequence to the second character sequence.

The Levenshtein distance between two alphanumeric sequences a and b can be computed as follows:

Where is the indicator function equal to 0 when , and equal to 1 otherwise.

We will now see examples based on Levenshtein distance.

Finding domain similarity between malicious URLs

The following code is a Python-based implementation of the iterative Levenshtein distance:

def iterative_levenshtein(a, b):  
    rows = len(a)+1  cols...
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