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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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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

Detecting anomalies in a network with k-means

In various network attacks, the malware floods the network with traffic. They use this as a means to get unauthorized access. Since network traffic usually is massive by volume, we will be using the k-means algorithm to detect anomalies.

K-means are suitable algorithms for such cases, as network traffic usually has a pattern. Also, network threats do not have labeled data. Every attack is different from the other. Hence, using unsupervised approaches is the best bet here. We will be using these methods to detect batches of traffic that stand out from the rest of the network traffic.

Network intrusion data

We will be using the KDD Cup 1999 data for this use case. The data is approximately...

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