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

Financial Fraud and How Deep Learning Can Mitigate It

Financial fraud is one of the major causes of monetary loss in banks and financial organizations. Rule-based fraud-detection systems are not capable of detecting advanced persistent threats. Such threats find ways to circumnavigate rule-based systems. Old signature-based methods establish in advance any fraudulent transactions such as loan default prediction, credit card fraud, cheque kiting or empty ATM envelope deposits.

In this chapter, we will see how machine learning can capture fraudulent transactions. We will cover the following major topics:

  • Machine learning to detect fraud
  • Imbalanced data
  • Handling data imbalances
  • Detecting credit card fraud
  • Using logistic regression to detect fraud
  • Analyzing the best approaches to detect fraud
  • Hyperparameter tuning to get the best model results