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

What is a time series?

A time series is defined as an array of data points that is arranged with respect to time. The data points are indicative of an activity that takes place at a time interval. One popular example is the total number of stocks that were traded at a certain time interval with other details like stock prices and their respective trading information at each second. Unlike a continuous time variable, these time series data points have a discrete value at different points of time. Hence, these are often referred to as discrete data variables. Time series data can be gathered over any minimum or maximum amount of time. There is no upper or lower bound to the period over which data is collected.

Time series data has the following:

  • Specific instances of time forming the timestamp
  • A start timestamp and an end timestamp
  • The total elapsed time for the instance