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
Basics of Machine Learning in Cybersecurity
Using Data Science to Catch Email Fraud and Spam

Email spoofing

Email spoofing involves masquerading as someone else in an email. The most common method of spoofing has the same sender's name, but masks the ID. In other words, the sender ID is forged. Email spoofing is possible when there are no valid methods for authenticating the sender's ID. A simple mail transfer protocol email consists of the following details:

Mail From:
Receipt to:
Sender's ID:

The following screenshot shows an email from PayPal for updating an account:

Bogus offers

There are also emails that try to sell us different commodities. They often include offers that seem too good to be true.

These offers could include items available before their actual release dates, such as the iPhone X...