Book Image

Hands-On Machine Learning for Cybersecurity

By : Halder, Sinan Ozdemir
Book Image

Hands-On Machine Learning for Cybersecurity

By: 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
1
Basics of Machine Learning in Cybersecurity
5
Using Data Science to Catch Email Fraud and Spam

Rootkit

Rootkits, like botnets, remotely access computers and do not get detected by the systems. Rootkits are enabled in such a fashion that the malware can be remotely executed by the malicious personnel. Roots access, modify, and delete files. They are used to steal information by staying concealed. Since rootkits are stealthy, they are extremely difficult to detect. Regular system updates are patches which are the only means to keep away from rootkits.