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

Use case

We will now discuss some of the earlier intrusions and injections that we have already discussed at the beginning of the chapter. For the purpose of our experiment, we will use the KDD Cup 1999 computer network intrusion detection dataset. The goal of this experiment is to distinguish between the good and bad network connections.

The dataset

The data sources are primarily sourced from the 1998 DARPA Intrusion Detection Evaluation Program by MIT Lincoln Labs. This dataset contains a variety of network events that have been simulated in the military network environment. The data is a TCP dump that has been accumulated from the local area network of an Air Force environment. The data is peppered with multiple attacks...