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.
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
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