Advanced Machine Learning for Cyber-Attack Detection in IoT Networks
eBook - ePub

Advanced Machine Learning for Cyber-Attack Detection in IoT Networks

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Advanced Machine Learning for Cyber-Attack Detection in IoT Networks

About this book

Advanced Machine Learning for Cyber-Attack Detection in IoT Networks analyzes diverse machine learning techniques, including supervised, unsupervised, reinforcement, and deep learning, along with their applications in detecting and preventing cyberattacks in future IoT systems. Chapters investigate the key challenges and vulnerabilities found in IoT security, how to handle challenges in data collection and pre-processing specific to IoT environments, as well as what metrics to consider for evaluating the performance of machine learning models. Other sections look at the training, validation, and evaluation of supervised learning models and present case studies and examples that demonstrate the application of supervised learning in IoT security. - Presents a comprehensive overview of research on IoT security threats and potential attacks - Investigates machine learning techniques, their mathematical foundations, and their application in cybersecurity - Presents metrics for evaluating the performance of machine learning models as well as benchmark datasets and evaluation frameworks for assessing IoT systems

Frequently asked questions

Yes, you can cancel anytime from the Subscription tab in your account settings on the Perlego website. Your subscription will stay active until the end of your current billing period. Learn how to cancel your subscription.
No, books cannot be downloaded as external files, such as PDFs, for use outside of Perlego. However, you can download books within the Perlego app for offline reading on mobile or tablet. Learn more here.
Perlego offers two plans: Essential and Complete
  • Essential is ideal for learners and professionals who enjoy exploring a wide range of subjects. Access the Essential Library with 800,000+ trusted titles and best-sellers across business, personal growth, and the humanities. Includes unlimited reading time and Standard Read Aloud voice.
  • Complete: Perfect for advanced learners and researchers needing full, unrestricted access. Unlock 1.4M+ books across hundreds of subjects, including academic and specialized titles. The Complete Plan also includes advanced features like Premium Read Aloud and Research Assistant.
Both plans are available with monthly, semester, or annual billing cycles.
We are an online textbook subscription service, where you can get access to an entire online library for less than the price of a single book per month. With over 1 million books across 1000+ topics, we’ve got you covered! Learn more here.
Look out for the read-aloud symbol on your next book to see if you can listen to it. The read-aloud tool reads text aloud for you, highlighting the text as it is being read. You can pause it, speed it up and slow it down. Learn more here.
Yes! You can use the Perlego app on both iOS or Android devices to read anytime, anywhere — even offline. Perfect for commutes or when you’re on the go.
Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app.
Yes, you can access Advanced Machine Learning for Cyber-Attack Detection in IoT Networks by Dinh Thai Hoang,Nguyen Quang Hieu,Diep N. Nguyen,Ekram Hossain in PDF and/or ePUB format, as well as other popular books in Computer Science & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Title of Book
  2. Chapter 1: Machine learning for cyber-attack detection in IoT networks: an overview
  3. Chapter 2: Evaluation and performance metrics for IoT security networks
  4. Chapter 3: Adversarial machine learning techniques for the industrial IoT paradigm
  5. Chapter 4: Federated learning for distributed intrusion detection in IoT networks
  6. Chapter 5: Safeguarding IoT networks with generative adversarial networks
  7. Chapter 6: Meta-learning for cyber-attack detection in IoT networks
  8. Chapter 7: Transfer learning with CNN for cyber-attack detection in IoT networks
  9. Chapter 8: Lightweight intrusion detection methods based on artificial intelligence for IoT networks
  10. Chapter 9: A new federated learning system with attention-aware aggregation method for intrusion detection systems
  11. Chapter 10: Enhancing intrusion detection using an improved sparrow search algorithm with deep learning in the Internet of Things environment
  12. Chapter 11: Advancing cyber-attack detection for in-vehicle networks: a comparative study of a machine learning-based intrusion detection system
  13. Chapter 12: Practical approaches towards IoT dataset generation for security experiments
  14. Chapter 13: Challenges and potential research directions for machine learning-based cyber-attack detection in IoT networks
  15. Index