Feature Selection and Feature Extraction in Machine Learning-Based IoT Intrusion Detection System

Language: English

Published by Eliva Press Apr 2024, 2024

9999317790 / 9789999317795

  • Softcover
  • New
See all details

Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.

5-star seller

AbeBooks seller since January 11, 2012

Softcover

Condition: New

US$ 45.44

US$ 26.15 shipping 
Ships from Germany to U.S.A.

Quantity: 2 available

Add to basket
Free 30-day returns

Item description from seller

This item is printed on demand - it takes 3-4 days longer - Neuware -'In a world increasingly reliant on Internet of Things (IoT) devices, ensuring their security is paramount. Yet, these very devices are vulnerable to cyberattacks, posing significant threats to individuals and organizations alike. To combat this, machine learning has emerged as a powerful tool for network intrusion detection in IoT environments.Delving deep into this intersection of cybersecurity and machine learning, this book presents a comprehensive exploration of feature reduction techniques for IoT network intrusion detection. Drawing from extensive research, it offers a meticulous comparison of feature extraction and selection methods within a machine learning-based attack classification framework.Through rigorous analysis of performance metrics such as accuracy, f1-score, and runtime, the book sheds light on the efficacy of these techniques on the heterogeneous IoT dataset known as Network TON-IoT. Unveiling key insights, it reveals that while feature extraction tends to outperform feature selection in detection performance, the latter exhibits advantages in model training and inference time.But the findings don't stop there. The book delves deeper into the nuances of IoT security, addressing the challenges posed by computational resource constraints. It underscores the importance of feature reduction in constructing lightweight yet effective intrusion detection models tailored for IoT scenarios.Moreover, the book offers practical guidance for selecting intrusion detection methods tailored to specific IoT environments. By analyzing the trade-offs between feature extraction and selection, it equips readers with the knowledge to navigate the complexities of IoT security.' 54 pp. Englisch.…

Seller Inventory # 9789999317795

Title
Feature Selection and Feature Extraction in Machine Learning-Based IoT Intrusion Detection System
Author
Jing Li
Publisher
Eliva Press Apr 2024
Publication year
2024
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
9999317790
ISBN 13
9789999317795
Item weight
95 grams
Dimensions
229x152x3 mm

BuchWeltWeit Ludwig Meier e.K.

Bergisch Gladbach, Germany

5-star seller

AbeBooks seller since January 11, 2012

Shipping rates from Germany to U.S.A.

Item5 to 15 business days5 to 15 business days
First itemUS$ 26.15US$ 26.15
Delivery times are set by sellers and vary by carrier and location. Orders passing through Customs may face delays and buyers are responsible for any associated duties or fees. Sellers may contact you regarding additional charges to cover any increased costs to ship your items.

Payment methods

  • Visa
  • Mastercard
  • American Express
  • Apple Pay
  • Google Pay
  • Bank Wire Transfer
  • Check
  • Paypal

Seller's business information

BuchWeltWeit Ludwig Meier e.K.

Germany