Hands-On Machine Learning for Cybersecurity: Safeguard your system by making your machines intelligent using the Python ecosystem

by Soma Halder, Sinan Ozdemir

Overview

Get into the world of smart data security using machine learning algorithms and Python libraries

Key Features

• Learn machine learning algorithms and cybersecurity fundamentals
  
  


• Automate your daily workflow by applying use cases to many facets of security
  
  


• Implement smart machine learning solutions to detect various cybersecurity problems
  
  

Book Description

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

What you will learn

• Use machine learning algorithms with complex datasets to implement cybersecurity concepts
  
  


• Implement machine learning algorithms such as clustering, k-means, and Naive Bayes to solve real-world problems
  
  


• Learn to speed up a system using Python libraries with NumPy, Scikit-learn, and CUDA
  
  


• Understand how to combat malware, detect spam, and fight financial fraud to mitigate cyber crimes
  
  


• Use TensorFlow in the cybersecurity domain and implement real-world examples
  
  


• Learn how machine learning and Python can be used in complex cyber issues

Who this book is for

This book is for the data scientists, machine learning developers, security researchers, and anyone keen to apply machine learning to up-skill computer security. Having some working knowledge of Python and being familiar with the basics of machine learning and cybersecurity fundamentals will help to get the most out of the book

Product Details

ISBN-13:	9781788990967
Publisher:	Packt Publishing
Publication date:	12/31/2018
Sold by:	Barnes & Noble
Format:	eBook
Pages:	318
File size:	11 MB
Note:	This product may take a few minutes to download.

About the Author

Soma Halder is the data science lead of the big data analytics group at Reliance Jio Infocomm Ltd, one of India's largest telecom companies. She specializes in analytics, big data, cybersecurity, and machine learning. She has approximately 10 years of machine learning experience, especially in the field of cybersecurity. She studied at the University of Alabama, Birmingham where she did her master's with an emphasis on Knowledge discovery and Data Mining and computer forensics. She has worked for Visa, Salesforce, and AT&T. She has also worked for start-ups, both in India and the US (E8 Security, Headway ai, and Norah ai). She has several conference publications to her name in the field of cybersecurity, machine learning, and deep learning. Sinan Ozdemir is a data scientist, start-up founder, and educator living in the San Francisco Bay Area. He studied pure mathematics at the Johns Hopkins University. He then spent several years conducting lectures on data science there, before founding his own start-up, Kylie ai, which uses artificial intelligence to clone brand personalities and automate customer service communications. He is also the author of Principles of Data Science, available through Packt.

Table of Contents

Table of Contents

1. Basics of Machine Learning in Cyber Security
2. Time series analysis and Ensemble modelling
3. Segregating legitimate and lousy URLs
4. Knocking down captchas
5. Using Data Science to catch email frauds and spams
6. Efficient Network Anomaly detection using K Means
7. Decision Tree and context based malicious event detection
8. Catching impersonators and hackers red handed
9. Change the game with Tensorflow
10. Financial frauds and how deep learning can mitigate them
11. Practical Case Studies in Cyber Security