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
Table of Contents (13 chapters)
Free Chapter
1
Basics of Machine Learning in Cybersecurity
5
Using Data Science to Catch Email Fraud and Spam

Logistic regression to detect malicious URLs

We will be using logistic regression to detect malicious URLs. Before we deal with the model, let's look at the dataset.

Dataset

We have the data in a comma-separated file. The first column is the URL and the second column identifies the label, stating whether the URL is good or bad. The dataset looks as follows:

url,label
diaryofagameaddict.com,bad
espdesign.com.au,bad
iamagameaddict.com,bad
kalantzis.net,bad
slightlyoffcenter.net,bad
toddscarwash.com,bad
tubemoviez.com,bad
ipl.hk,bad
crackspider.us/toolbar/install.php?pack=exe,bad
pos-kupang.com/,bad
rupor.info,bad
svision-online.de/mgfi/administrator/components/com_babackup/classes/fx29id1.txt,bad
officeon.ch.ma/office.js?google_ad_format...