A Comparison of Feature Selection and Feature Extraction in Network Intrusion Detection Systems

Tuan-Cuong Vuong , Hung Tran , Mai Xuan Trang , Vu-Duc Ngo , Thien Van Luong

2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) (2022) Conference

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Abstract

Internet of Things (IoT) has been playing an important role in many sectors, such as smart cities, smart agriculture, smart healthcare, and smart manufacturing. However, IoT devices are 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 to machine learning models. This aims to make the detection complexity low enough for real-time operations, which is particularly vital in intrusion detection systems. This paper provides a comprehensive comparison between these two methods in terms of various performance metrics, namely, precision rate, recall rate, detection accuracy, as well as runtime complexity.

Abstract

Internet of Things (IoT) has been playing an important role in many sectors, such as smart cities, smart agriculture, smart healthcare, and smart manufacturing. However, IoT devices are 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 to machine learning models.

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

Key Contributions

  1. Comparative Analysis: First comprehensive comparison of feature selection vs extraction in IoT intrusion detection
  2. Performance Metrics: Evaluation using precision, recall, detection accuracy, and runtime complexity
  3. Real-time Focus: Emphasis on making detection suitable for real-time IoT environments
  4. Practical Guidelines: Recommendations for method selection based on specific use cases

Conference Details

  • Conference: APSIPA ASC 2022 (Asia-Pacific Signal and Information Processing Association Annual Summit and Conference)
  • Location: Chiang Mai, Thailand
  • Date: November 7-10, 2022
  • Pages: 1798-1804
  • Publisher: IEEE

Keywords

IoT Networks, Intrusion Detection Systems, Feature Selection, Feature Extraction, Machine Learning, Cybersecurity

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