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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

Use case

We will now discuss some of the earlier intrusions and injections that we have already discussed at the beginning of the chapter. For the purpose of our experiment, we will use the KDD Cup 1999 computer network intrusion detection dataset. The goal of this experiment is to distinguish between the good and bad network connections.

The dataset

The data sources are primarily sourced from the 1998 DARPA Intrusion Detection Evaluation Program by MIT Lincoln Labs. This dataset contains a variety of network events that have been simulated in the military network environment. The data is a TCP dump that has been accumulated from the local area network of an Air Force environment. The data is peppered with multiple attacks...

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