Active Learning for Wireless IoT Intrusion Detection

TL;DR

This paper explores a human-in-the-loop active learning approach to improve wireless IoT intrusion detection, demonstrating significant performance gains over traditional methods and highlighting the potential for further research in this emerging area.

Contribution

It introduces the application of active learning with human-in-the-loop in wireless IoT intrusion detection, addressing key challenges and showing improved detection performance.

Findings

Active learning significantly outperforms traditional supervised learning in intrusion detection.

Human-in-the-loop approach enhances detection accuracy and adaptability.

The method demonstrates promising results in experimental scenarios.

Abstract

Internet of Things (IoT) is becoming truly ubiquitous in our everyday life, but it also faces unique security challenges. Intrusion detection is critical for the security and safety of a wireless IoT network. This paper discusses the human-in-the-loop active learning approach for wireless intrusion detection. We first present the fundamental challenges against the design of a successful Intrusion Detection System (IDS) for wireless IoT network. We then briefly review the rudimentary concepts of active learning and propose its employment in the diverse applications of wireless intrusion detection. Experimental example is also presented to show the significant performance improvement of the active learning method over traditional supervised learning approach. While machine learning techniques have been widely employed for intrusion detection, the application of human-in-the-loop machine learning that leverages both machine and human intelligence to intrusion detection of IoT is still in its infancy. We hope this article can assist the readers in understanding the key concepts of active learning and spur further research in this area.