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

Using heuristics to detect malicious pages

We have already discussed the different kinds of URLs, such as benign URLs, spam URLs, and malicious URLs. In the following exercise, we will categorize some URLs, thereby making a prediction of the type of pages that they would redirect us to. Benign URLs always drives us to benign sites. Spam URLs either lead us to a command and control server or a spam website that tries to sell us unsolicited items. Malicious URLs lead us to sites that install some kind of malware on our systems. Since the system does not actually visit the pages of the websites that the URL points to, we are able to save a lot of resources in terms of latency and get a better performance out of our computer.

Data for the analysis