Advanced Machine Learning for Cyber-Attack Detection in IoT Networks
- Publisher's listprice EUR 177.99
-
75 503 Ft (71 907 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 10% (cc. 7 550 Ft off)
- Discounted price 67 952 Ft (64 716 Ft + 5% VAT)
75 503 Ft
Availability
Not yet published.
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Publisher Academic Press
- Date of Publication 1 June 2025
- ISBN 9780443290329
- Binding Paperback
- No. of pages300 pages
- Size 234x190 mm
- Language English 700
Categories
Long description:
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.
MoreTable of Contents:
1. Machine Learning for Cyber-Attack Detection in IoT Networks: An Overview
2. Evaluation and Performance Metrics for IoT Security Networks
3. Adversarial Machine Learning Techniques for the Industrial IoT Paradigm
4. Federated Learning for Distributed Intrusion Detection in IoT Networks
5. Safeguarding IoT Networks with Generative Adversarial Networks
6. Meta-Learning for Cyber-Attack Detection in IoT Networks
7. Transfer Learning with CNN for Cyberattack Detection in IoT Networks
8. Lightweight Intrusion Detection Methods Based on Artificial Intelligence for IoT Networks
9. A New Federated Learning System with Attention-Aware Aggregation Method for Intrusion Detection Systems
10. Enhancing Intrusion Detection using Improved Sparrow Search Algorithm with Deep Learning on Internet of Things Environment
11. Advancing Cyberattack Detection for In-Vehicle Network: A Comparative Study of Machine Learning-based Intrusion Detection System
12. Practical Approaches Towards IoT Dataset Generation for Security Experiments
13. Challenges and Potential Research Directions for Machine Learning-based Cyber-Attack Detection in IoT Networks
