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

Using Data Science to Catch Email Fraud and Spam

Fraudulent emails are deceptive measures that are taken up by goons for personal gain in order to lure in innocent people. They are used to scam and defraud people. The emails usually involve offers that are too good to be true, and they are targeted towards naive individuals.

In this chapter, we will describe how spam emails work, and we will list a few machine learning algorithms that can mitigate the problem. The chapter will be divided into the following sub-sections:

  • Fraudulent emails and spoofs
  • Types of email fraud
  • Spam detection using the Naive Bayes algorithm
  • Featurization techniques that convert text-based emails into numeric values
  • Spam detection with logistic regression