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
Sprache
Verlagsort
Dateigröße
ISBN-13
978-0-443-29033-6 (9780443290336)
Schweitzer Klassifikation
1. Machine Learning for Cyber-Attack Detection in IoT Networks: An Overview2. Evaluation and Performance Metrics for IoT Security Networks3. Adversarial Machine Learning Techniques for the Industrial IoT Paradigm4. Federated Learning for Distributed Intrusion Detection in IoT Networks5. Safeguarding IoT Networks with Generative Adversarial Networks6. Meta-Learning for Cyber-Attack Detection in IoT Networks7. Transfer Learning with CNN for Cyberattack Detection in IoT Networks8. Lightweight Intrusion Detection Methods Based on Artificial Intelligence for IoT Networks9. A New Federated Learning System with Attention-Aware Aggregation Method for Intrusion Detection Systems10. Enhancing Intrusion Detection using Improved Sparrow Search Algorithm with Deep Learning on Internet of Things Environment11. Advancing Cyberattack Detection for In-Vehicle Network: A Comparative Study of Machine Learning-based Intrusion Detection System12. Practical Approaches Towards IoT Dataset Generation for Security Experiments13. Challenges and Potential Research Directions for Machine Learning-based Cyber-Attack Detection in IoT Networks
