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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

Introduction to the types of abnormalities in URLs

Lousy URLs are URLs that have been created with malicious intent. They are often the precursors to cyberattacks that may happen in the near future. Lousy URLs can hit pretty close to home, leaving each one of us very vulnerable to bad sites that we might visit on purpose or by accident.

Google often has inbuilt malicious URL detection capabilities, and the following screenshot shows what many of us have bumped into upon detecting a malicious URL:

Malicious URLs lead us to bad websites that either try to sell us counterfeit products, such as medication, unsolicited products, such as watches from Rolex, and so on. These websites might sell a variety of items, such as screensavers for your computer and funny pictures.

Bad URLs may also lead to phishing sites—that is, sites that imitate real websites, such as banks and credit...

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