Hands-on Machine Learning for Cybersecurity

Get into the world of smart data security using the power of machine learning algorithms

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Hands-on Machine Learning for Cybersecurity

Soma Halder

Get into the world of smart data security using the power of machine learning algorithms

Access cutting-edge content as it's created

Want access to this book right now? Read as we develop it as part of our Early Access program. Click here to find out more about Early Access.

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Book Details

ISBN 139781788992282
Paperback424 pages

Book Description

Machine Learning is a growing trend in every technological field including computer security. Many research and practical applications are in line which has a potential to change the way how data is secured. With this book, you will stand a chance to mark your developments in cyber security domain using machine learning capabilities.

This book begins with giving you the basics of machine learning in cyber security using python and their extensive libraries support. You will explore various machine learning domains such as time series analysis, ensemble modeling to get your foundations right. You will implement your learning in various examples such as building system to identify malicious URLs, bypass defensive technologies, and build a program for detecting email frauds and spam using supervised learning and Naive Bayes algorithm. Later you will learn to make effective use of K means algorithm, to develop a solution to detect and alert any malicious activity going on the network. Next, you will be building weightless and complex decision tree and you will implement Digital biometrics and fingerprint from users interaction 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 in creating models and training the system from previous fraudulent events so that they can be mitigated in future.

By the end of this book, you will be able to build, apply, and evaluate machine learning algorithms to identify potential threats such as intrusion detection and malware. You will be introduced to cutting-edge big data tools and GPU processing to show how these techniques can be applied to extremely large data sets to detect traffic and end-point behavior.

Table of Contents

Chapter 1: Basics of Machine Learning in Cyber Security
Chapter 4: Knocking Down Captchas
Chapter 5: Using Data Science to Catch Email Frauds and Spams
Chapter 6: Efficient Network Anomaly Detection Using K Means
Chapter 7: Decision Tree and Context Based Malicious Event Detection
Chapter 8: Catching Impersonators and Hackers Red Handed
Chapter 9: Speeding Things Up with GPU
Chapter 10: Change the Game with TensorFlow
Chapter 11: Financial Frauds and How Deep Learning Can Mitigate them

What You Will Learn

  • Gain the knowledge on using machine learning algorithms to get started with the concepts in cyber security using complex datasets.
  • Solve real world concerns of cyber security using Machine learning algorithms such as Clustering, K means, Linear regression, Naive Bayes etc
  • Explore the beauty of Digital biometrics and fingerprinting for validating whether the user is impersonator or a legitimate user.
  • Learn how to speed up the system using Python GPU libraries with NumPY, Scikit-learn and CUDA programs
  • Learn to use deep learning in detecting financial frauds and train your system effectively so that they can be mitigated in future.
  • Understand the power of Tensorflow in cybersecurity domain and implement real world examples

Authors

Table of Contents

Chapter 1: Basics of Machine Learning in Cyber Security
Chapter 4: Knocking Down Captchas
Chapter 5: Using Data Science to Catch Email Frauds and Spams
Chapter 6: Efficient Network Anomaly Detection Using K Means
Chapter 7: Decision Tree and Context Based Malicious Event Detection
Chapter 8: Catching Impersonators and Hackers Red Handed
Chapter 9: Speeding Things Up with GPU
Chapter 10: Change the Game with TensorFlow
Chapter 11: Financial Frauds and How Deep Learning Can Mitigate them

Book Details

ISBN 139781788992282
Paperback424 pages
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