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Mastering Machine Learning for Penetration Testing

You're reading from  Mastering Machine Learning for Penetration Testing

Product type Book
Published in Jun 2018
Publisher Packt
ISBN-13 9781788997409
Pages 276 pages
Edition 1st Edition
Languages
Author (1):
Chiheb Chebbi Chiheb Chebbi
Profile icon Chiheb Chebbi
Toc

Table of Contents (13) Chapters close

Preface 1. Introduction to Machine Learning in Pentesting 2. Phishing Domain Detection 3. Malware Detection with API Calls and PE Headers 4. Malware Detection with Deep Learning 5. Botnet Detection with Machine Learning 6. Machine Learning in Anomaly Detection Systems 7. Detecting Advanced Persistent Threats 8. Evading Intrusion Detection Systems 9. Bypassing Machine Learning Malware Detectors 10. Best Practices for Machine Learning and Feature Engineering 11. Assessments 12. Other Books You May Enjoy

Building your own IDS

By now, you know the different network anomaly detection techniques. We are now going to build our own network IDS with Python, from scratch. The University of California hosted a competition called The Third International Knowledge Discovery and Data Mining Tools Competition, and they provided a dataset called KDD Cup 1999 Data, or KDD 1990. You can find it at http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.

The main aim of the competition was building a system that was able to distinguish between bad (attack) and good (normal) connections. Many modern proposals and machine learning solutions were made using the dataset. But as you can see, the dataset is old; the models were not able to detect modern network attacks, in addition to other issues, like data redundancy. A great study called A Detailed Analysis of the KDD CUP 99 Data Set, done by Mahbod...

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