An adversarial domain adaptation approach combining dual domain pairing strategy for IoT intrusion detection under few-shot samples

Abstract

The security of the Internet of Things (IoT) is an integral research topic to support its applicability. With the rapid development of IoT technology, security threats have become increasingly serious. Attackers turn their attention to large IoT devices with many security vulnerabilities, and many infected IoT devices are used to form botnets to launch intrusions. Furthermore, IoT communication networks poses security risks. Therefore, it is necessary to design a model for the IoT intrusion detection. However, the current models are trained using big data samples. Moreover, IoT devices can only intercept a few-shot samples in special scenarios, resulting in lower efficiency and accuracy. In this study, we propose an adversarial domain adaptation (ADDA) approach with dual domain pairing strategy for IoT intrusion detection under few-shot samples. First, we design two data augmentation algorithms to construct a sample transfer model that conforms to the joint dual domain distribution. We then combine the dual domain pairing strategy and ADDA model to perform supervised domain adaptation. In addition, a lightweight network is designed to extract features. Finally, a new loss function and domain discriminator model are constructed to achieve IoT intrusion detection under few-shot samples. Different results demonstrate the effectiveness of our model.