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Hands-On Machine Learning for Cybersecurity

You're reading from   Hands-On Machine Learning for Cybersecurity Safeguard your system by making your machines intelligent using the Python ecosystem

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781788992282
Length 318 pages
Edition 1st Edition
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Authors (2):
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Soma Halder Soma Halder
Author Profile Icon Soma Halder
Soma Halder
Sinan Ozdemir Sinan Ozdemir
Author Profile Icon Sinan Ozdemir
Sinan Ozdemir
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Toc

Table of Contents (13) Chapters Close

Preface 1. Basics of Machine Learning in Cybersecurity 2. Time Series Analysis and Ensemble Modeling FREE CHAPTER 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

Using heuristics to detect malicious pages

We have already discussed the different kinds of URLs, such as benign URLs, spam URLs, and malicious URLs. In the following exercise, we will categorize some URLs, thereby making a prediction of the type of pages that they would redirect us to. Benign URLs always drives us to benign sites. Spam URLs either lead us to a command and control server or a spam website that tries to sell us unsolicited items. Malicious URLs lead us to sites that install some kind of malware on our systems. Since the system does not actually visit the pages of the websites that the URL points to, we are able to save a lot of resources in terms of latency and get a better performance out of our computer.

Data for the analysis

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