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

Detecting anomalies in a network with k-means

In various network attacks, the malware floods the network with traffic. They use this as a means to get unauthorized access. Since network traffic usually is massive by volume, we will be using the k-means algorithm to detect anomalies.

K-means are suitable algorithms for such cases, as network traffic usually has a pattern. Also, network threats do not have labeled data. Every attack is different from the other. Hence, using unsupervised approaches is the best bet here. We will be using these methods to detect batches of traffic that stand out from the rest of the network traffic.

Network intrusion data

We will be using the KDD Cup 1999 data for this use case. The data is approximately...