Machine learning-based intrusion detection: feature selection versus feature extraction

Abstract

Internet of Things (IoTs) has been playing an important role in many sectors, such as smart cities, smart agriculture, smart healthcare, and smart manufacturing. However, IoT devices are highly vulnerable to cyber-attacks, which may result in security breaches and data leakages. To effectively prevent these attacks, a variety of machine learning-based network intrusion detection methods for IoT networks have been developed, which often rely on either feature extraction or feature selection techniques for reducing the dimension of input data before being fed into machine learning models.

This aims to make the detection complexity low enough for real-time operations, which is particularly vital in any intrusion detection systems. This paper provides a comprehensive comparison between these two feature reduction methods of intrusion detection in terms of various performance metrics, namely, precision rate, recall rate, detection accuracy, as well as runtime complexity.

Key Contributions

1. Comprehensive Comparison: A detailed analysis of feature selection vs feature extraction techniques in IoT intrusion detection
2. Performance Evaluation: Evaluation across multiple metrics including precision, recall, accuracy, and runtime complexity
3. Real-time Considerations: Focus on making detection suitable for real-time IoT operations
4. Practical Insights: Guidelines for choosing appropriate feature reduction methods based on specific requirements

Keywords

IoT Security, Intrusion Detection, Feature Selection, Feature Extraction, Machine Learning, Cybersecurity