56,33 €
62,59 €
-10% with code: EXTRA
Machine Learning in Cyber Security
Machine Learning in Cyber Security
56,33
62,59 €
  • We will send in 10–14 business days.
This book is addressed for both seasoned and beginners in the field of machine learning, we included a simple explanation for each idea and then we expanded to all technical details. We started by explaining KNN and all its challenges. Then we introduced a newly discovered dataset deficiency and an enhancement to counter that problem. The field of the experiment was on network traffic classification. We combined the precision of the DPI method and the privacy of blind classifiers, once the mode…
62.59
  • Publisher:
  • ISBN-10: 1636480764
  • ISBN-13: 9781636480763
  • Format: 15.2 x 22.9 x 0.3 cm, minkšti viršeliai
  • Language: English
  • SAVE -10% with code: EXTRA

Machine Learning in Cyber Security (e-book) (used book) | bookbook.eu

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This book is addressed for both seasoned and beginners in the field of machine learning, we included a simple explanation for each idea and then we expanded to all technical details. We started by explaining KNN and all its challenges. Then we introduced a newly discovered dataset deficiency and an enhancement to counter that problem. The field of the experiment was on network traffic classification. We combined the precision of the DPI method and the privacy of blind classifiers, once the model is trained on known traffic flows, then we used the statistical data and the packet header for classification.

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  • Author: Jawad Khalife
  • Publisher:
  • ISBN-10: 1636480764
  • ISBN-13: 9781636480763
  • Format: 15.2 x 22.9 x 0.3 cm, minkšti viršeliai
  • Language: English English

This book is addressed for both seasoned and beginners in the field of machine learning, we included a simple explanation for each idea and then we expanded to all technical details. We started by explaining KNN and all its challenges. Then we introduced a newly discovered dataset deficiency and an enhancement to counter that problem. The field of the experiment was on network traffic classification. We combined the precision of the DPI method and the privacy of blind classifiers, once the model is trained on known traffic flows, then we used the statistical data and the packet header for classification.

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