BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

Machine Learning in Cyber Security: Network Traffic Classification based on Class Weight-based K-NN Classifier (CWK-NN)

Machine Learning in Cyber Security: Network Traffic Classification based on Class Weight-based K-NN Classifier (CWK-NN)

Machine Learning in Cyber Security: Network Traffic Classification based on
Stock photo: cover may vary

Machine Learning in Cyber Security: Network Traffic Classification based on Class Weight-based K-NN Classifier (CWK-NN) Paperback - 2021

by Hijazi, Mohamad Osama

Add to wish list
  • Used
  • Good
  • Paperback
Used - Good

Description

paperback. Good. Access codes and supplements are not guaranteed with used items. May be an ex-library book.
Ask the seller a question Add to wish list
A$81.10
Free Delivery within USA
Standard delivery: 7 to 14 days
More delivery options
Dropship order
Ships from Bonita (California, United States)

Details

  • Title Machine Learning in Cyber Security: Network Traffic Classification based on Class Weight-based K-NN Classifier (CWK-NN)
  • Author Hijazi, Mohamad Osama
  • Binding Paperback
  • Condition Used - Good
  • Pages 52
  • Volumes 1
  • Language ENG
  • Publisher Eliva Press
  • Publication date 2021-01-07
  • Bookseller's Inventory # 1636480764.G
  • ISBN 9781636480763 / 1636480764
  • Weight 0.18 lbs (0.08 kg)
  • Dimensions 9.02 x 5.98 x 0.11 in (22.91 x 15.19 x 0.28 cm)
  • Category Computers - Computer Security
  • Quantity available 1

About Bonita California, United States

Biblio member since 2020

Terms of Sale: 30 day return guarantee, with full refund including original shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.

Browse books from Bonita

Reader reviews for Machine Learning in Cyber Security: Network Traffic Classification based on Class Weight-based K-NN Classifier (CWK-NN)

From the publisher

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.
tracking-