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Hands-On Machine Learning for Cybersecurity

You're reading from  Hands-On Machine Learning for Cybersecurity

Product type Book
Published in Dec 2018
Publisher Packt
ISBN-13 9781788992282
Pages 318 pages
Edition 1st Edition
Languages
Authors (2):
Soma Halder Soma Halder
Profile icon Soma Halder
Sinan Ozdemir Sinan Ozdemir
Profile icon Sinan Ozdemir
View More author details
Toc

Table of Contents (13) Chapters close

Preface 1. Basics of Machine Learning in Cybersecurity 2. Time Series Analysis and Ensemble Modeling 3. Segregating Legitimate and Lousy URLs 4. Knocking Down CAPTCHAs 5. Using Data Science to Catch Email Fraud and Spam 6. Efficient Network Anomaly Detection Using k-means 7. Decision Tree and Context-Based Malicious Event Detection 8. Catching Impersonators and Hackers Red Handed 9. Changing the Game with TensorFlow 10. Financial Fraud and How Deep Learning Can Mitigate It 11. Case Studies 12. Other Books You May Enjoy

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

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