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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

Efficient Network Anomaly Detection Using k-means

Network attacks are on the rise, and a lot of research work has been done to thwart the negative effects from such attacks. As discussed in the previous chapters, we identify attacks as any unauthorized attempt to do the following:

  • Get hold of information
  • Modify information
  • Disrupt services
  • Perform distributed denial of service to and from the server where information is stored
  • Exploit using malware and viruses
  • Privilege escalation and credential compromise

Network anomalies are unlike regular network infections by viruses. In such cases, network anomalies are detected by identifying non-conforming patterns in the network data. Not just network intrusion detection, such methods can also be used for other forms of outlier detection such as credit fraud, traffic violation detection, and customer churn detection.

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