Few-shot IoT attack detection based on SSDSAE and adaptive loss weighted meta residual network

Abstract

The Internet of Things (IoT) is an open and comprehensive network of smart objects. Unfortunately, it is also becoming increasingly vulnerable to security attacks during the increasing popularity of the IoT. It can lead traditional antivirus software to be less likely to prevent this threat. Therefore, it is necessary to design a model for the IoT attack detection. Current detection models are trained using massive big data samples. However, the distribution of traffic samples is few in specific scenarios. Also, existing models are also susceptible to noise interference in IoT environments, lowering detection efficiency and accuracy. In this work, we propose a few-shot IoT attack detection approach using a semi-supervised deep sparse autoencoder (SSDSAE) and an adaptive loss weighted meta residual network (ALWM-ResNet). First, an SSDSAE feature extraction model for local graph embedding is designed using local and non-local graph embedding constraints. Then, we design an ALWM-ResNet model to achieve IoT attack detection with few-shot samples under noise labels. A weighted function map is established using a weighted network and a meta-model, and weights are adaptively learned from the noise labels. Finally, we validate our approach using four IoT datasets. Several experimental results demonstrate the superior performance of our approach in IoT attack detection under few-shot samples.