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Machine Learning in Cyber Security
Machine Learning in Cyber Security
Knygos.lt klubas Knygos.lt nariams
41,50 €
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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 mode…
  • Leidėjas:
  • ISBN-10: 1636480764
  • ISBN-13: 9781636480763
  • Formatas: 15.2 x 22.9 x 0.3 cm, minkšti viršeliai
  • Kalba: Anglų

Machine Learning in Cyber Security (el. knyga) (skaityta knyga) | knygos.lt

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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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  • Autorius: Jawad Khalife
  • Leidėjas:
  • ISBN-10: 1636480764
  • ISBN-13: 9781636480763
  • Formatas: 15.2 x 22.9 x 0.3 cm, minkšti viršeliai
  • Kalba: Anglų

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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