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

Stages of a network attack

Before moving on to methods of intrusion detection, we will deal with multiple methods of network threats. To understand the details of network anomaly, we will discuss the six stages of cyber attacks.

Phase 1 – Reconnaissance

This is the very first stage of a network attack, where the vulnerabilities and potential targets are identified. Once the assessing of the vulnerabilities and the measure of the defenses are done, a weapon is chosen, and it could vary from being a phishing attack, a zero-day attack, or some other form of malware attack.

Phase 2 – Initial compromise