Active Learning for Wireless IoT Intrusion Detection
Kai Yang, Jie Ren, Yanqiao Zhu, Weiyi Zhang · IEEE Wireless Communications · 2018
The Internet of Things (IoT) is becoming truly ubiquitous in our everyday lives, but it also faces unique security challenges. Intrusion detection is critical for the security and safety of a wireless IoT network. This article discusses the human-in-theloop active learning approach for wireless intrusion detection. We first present the fundamental challenges against the design of a successful intrusion detection system for a 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. An experimental example is also presented to show the significant performance improvement of the active learning method over the traditional supervised learning approach. While machine learning (ML) techniques have been widely employed for intrusion detection, the application of human-in-the-loop ML that leverages both machine and human intelligence to intrusion detection of IoT is still in its infancy. We hope this article can assist readers in understanding the key concepts of active learning and spur further research in this area.