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

Machine learning to detect financial fraud

Machine learning helps us flag or predict fraud based on historical data. The most common method for fraud-detection is classification. For a classification problem, a set of data is mapped to a subset based on the category it belongs to. The training set helps to determine to which subset a dataset belongs. These subsets are often known as classes:

In cases of fraudulent transactions, the classification between legitimate and non-legitimate transactions is determined by the following parameters:

  • The amount of the transaction
  • The merchant where the transaction is made
  • The location where the transaction is made
  • The time of the transaction
  • Whether this was an in-person or online transaction

Imbalanced data